{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b6c0e9a1",
   "metadata": {},
   "source": [
    "# Прогноз смертности персонажей\n",
    "\n",
    "Загрузим данные и кратко проверим их размер, состав признаков и наличие пропусков."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c8a7b40b",
   "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>S.No</th>\n",
       "      <th>actual</th>\n",
       "      <th>pred</th>\n",
       "      <th>alive</th>\n",
       "      <th>plod</th>\n",
       "      <th>name</th>\n",
       "      <th>title</th>\n",
       "      <th>male</th>\n",
       "      <th>culture</th>\n",
       "      <th>dateOfBirth</th>\n",
       "      <th>...</th>\n",
       "      <th>isAliveHeir</th>\n",
       "      <th>isAliveSpouse</th>\n",
       "      <th>isMarried</th>\n",
       "      <th>isNoble</th>\n",
       "      <th>age</th>\n",
       "      <th>numDeadRelations</th>\n",
       "      <th>boolDeadRelations</th>\n",
       "      <th>isPopular</th>\n",
       "      <th>popularity</th>\n",
       "      <th>isAlive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.946</td>\n",
       "      <td>Viserys II Targaryen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.605351</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.613</td>\n",
       "      <td>Walder Frey</td>\n",
       "      <td>Lord of the Crossing</td>\n",
       "      <td>1</td>\n",
       "      <td>Rivermen</td>\n",
       "      <td>208.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.896321</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.507</td>\n",
       "      <td>Addison Hill</td>\n",
       "      <td>Ser</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.267559</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.924</td>\n",
       "      <td>Aemma Arryn</td>\n",
       "      <td>Queen</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.183946</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.383</td>\n",
       "      <td>Sylva Santagar</td>\n",
       "      <td>Greenstone</td>\n",
       "      <td>0</td>\n",
       "      <td>Dornish</td>\n",
       "      <td>276.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   S.No  actual  pred  alive   plod                  name  \\\n",
       "0     1       0     0  0.054  0.946  Viserys II Targaryen   \n",
       "1     2       1     0  0.387  0.613           Walder Frey   \n",
       "2     3       1     0  0.493  0.507          Addison Hill   \n",
       "3     4       0     0  0.076  0.924           Aemma Arryn   \n",
       "4     5       1     1  0.617  0.383        Sylva Santagar   \n",
       "\n",
       "                  title  male   culture  dateOfBirth  ...  isAliveHeir  \\\n",
       "0                   NaN     1       NaN          NaN  ...          0.0   \n",
       "1  Lord of the Crossing     1  Rivermen        208.0  ...          NaN   \n",
       "2                   Ser     1       NaN          NaN  ...          NaN   \n",
       "3                 Queen     0       NaN         82.0  ...          NaN   \n",
       "4            Greenstone     0   Dornish        276.0  ...          NaN   \n",
       "\n",
       "  isAliveSpouse isMarried isNoble   age numDeadRelations  boolDeadRelations  \\\n",
       "0           NaN         0       0   NaN               11                  1   \n",
       "1           1.0         1       1  97.0                1                  1   \n",
       "2           NaN         0       1   NaN                0                  0   \n",
       "3           0.0         1       1  23.0                0                  0   \n",
       "4           1.0         1       1  29.0                0                  0   \n",
       "\n",
       "   isPopular  popularity  isAlive  \n",
       "0          1    0.605351        0  \n",
       "1          1    0.896321        1  \n",
       "2          0    0.267559        1  \n",
       "3          0    0.183946        0  \n",
       "4          0    0.043478        1  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('../data/character-predictions.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cac21678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1946, 33)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "673e8cf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['S.No', 'actual', 'pred', 'alive', 'plod', 'name', 'title', 'male',\n",
       "       'culture', 'dateOfBirth', 'DateoFdeath', 'mother', 'father', 'heir',\n",
       "       'house', 'spouse', 'book1', 'book2', 'book3', 'book4', 'book5',\n",
       "       'isAliveMother', 'isAliveFather', 'isAliveHeir', 'isAliveSpouse',\n",
       "       'isMarried', 'isNoble', 'age', 'numDeadRelations', 'boolDeadRelations',\n",
       "       'isPopular', 'popularity', 'isAlive'],\n",
       "      dtype='str')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f0ca912",
   "metadata": {},
   "source": [
    "`S.No` - порядковый номер строки \n",
    "\n",
    "`actual` - фактический статус персонажа: 1 — жив, 0 — мёртв\n",
    "\n",
    "`pred` - предсказанный другой моделью статус персонажа\n",
    "\n",
    "`alive` - предсказанная другой моделью вероятность того, что персонаж жив\n",
    "\n",
    "`plod` - предсказанная другой моделью вероятность смерти персонажа\n",
    "\n",
    "`name` - имя персонажа\n",
    "\n",
    "`title` - титул персонажа\n",
    "\n",
    "`male` - пол персонажа: 1 — мужчина, 0 — женщина\n",
    "\n",
    "`culture` - культура или происхождение персонажа\n",
    "\n",
    "`dateOfBirth` - год рождения персонажа\n",
    "\n",
    "`DateoFdeath` - год смерти персонажа\n",
    "\n",
    "`mother` - имя матери персонажа\n",
    "\n",
    "`father` - имя отца персонажа\n",
    "\n",
    "`heir` - наследник персонажа\n",
    "\n",
    "`house` - дом, семья или организация персонажа\n",
    "\n",
    "`spouse` - супруг или супруга персонажа\n",
    "\n",
    "`book1` - появляется ли персонаж в книге *A Game of Thrones*\n",
    "\n",
    "`book2` - появляется ли персонаж в книге *A Clash of Kings*\n",
    "\n",
    "`book3` - появляется ли персонаж в книге *A Storm of Swords*\n",
    "\n",
    "`book4` - появляется ли персонаж в книге *A Feast for Crows*\n",
    "\n",
    "`book5` - появляется ли персонаж в книге *A Dance with Dragons*\n",
    "\n",
    "`isAliveMother` - жива ли мать персонажа\n",
    "\n",
    "`isAliveFather` - жив ли отец персонажа\n",
    "\n",
    "`isAliveHeir` - жив ли наследник персонажа\n",
    "\n",
    "`isAliveSpouse` - жив ли супруг или супруга персонажа\n",
    "\n",
    "`isMarried` - состоит ли персонаж в браке\n",
    "\n",
    "`isNoble` - является ли персонаж представителем знати\n",
    "\n",
    "`age` - возраст персонажа\n",
    "\n",
    "`numDeadRelations` - количество умерших родственников персонажа\n",
    "\n",
    "`boolDeadRelations` - есть ли у персонажа хотя бы один умерший родственник\n",
    "\n",
    "`isPopular` - считается ли персонаж популярным\n",
    "\n",
    "`popularity` - числовой показатель популярности персонажа от 0 до 1\n",
    "\n",
    "`isAlive` - фактический финальный статус персонажа: 1 — жив, 0 — мёртв\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4bbd4dea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 1946 entries, 0 to 1945\n",
      "Data columns (total 33 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   S.No               1946 non-null   int64  \n",
      " 1   actual             1946 non-null   int64  \n",
      " 2   pred               1946 non-null   int64  \n",
      " 3   alive              1946 non-null   float64\n",
      " 4   plod               1946 non-null   float64\n",
      " 5   name               1946 non-null   str    \n",
      " 6   title              938 non-null    str    \n",
      " 7   male               1946 non-null   int64  \n",
      " 8   culture            677 non-null    str    \n",
      " 9   dateOfBirth        433 non-null    float64\n",
      " 10  DateoFdeath        444 non-null    float64\n",
      " 11  mother             21 non-null     str    \n",
      " 12  father             26 non-null     str    \n",
      " 13  heir               23 non-null     str    \n",
      " 14  house              1519 non-null   str    \n",
      " 15  spouse             276 non-null    str    \n",
      " 16  book1              1946 non-null   int64  \n",
      " 17  book2              1946 non-null   int64  \n",
      " 18  book3              1946 non-null   int64  \n",
      " 19  book4              1946 non-null   int64  \n",
      " 20  book5              1946 non-null   int64  \n",
      " 21  isAliveMother      21 non-null     float64\n",
      " 22  isAliveFather      26 non-null     float64\n",
      " 23  isAliveHeir        23 non-null     float64\n",
      " 24  isAliveSpouse      276 non-null    float64\n",
      " 25  isMarried          1946 non-null   int64  \n",
      " 26  isNoble            1946 non-null   int64  \n",
      " 27  age                433 non-null    float64\n",
      " 28  numDeadRelations   1946 non-null   int64  \n",
      " 29  boolDeadRelations  1946 non-null   int64  \n",
      " 30  isPopular          1946 non-null   int64  \n",
      " 31  popularity         1946 non-null   float64\n",
      " 32  isAlive            1946 non-null   int64  \n",
      "dtypes: float64(10), int64(15), str(8)\n",
      "memory usage: 501.8 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "77b8f02b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S.No                    0\n",
       "actual                  0\n",
       "pred                    0\n",
       "alive                   0\n",
       "plod                    0\n",
       "name                    0\n",
       "title                1008\n",
       "male                    0\n",
       "culture              1269\n",
       "dateOfBirth          1513\n",
       "DateoFdeath          1502\n",
       "mother               1925\n",
       "father               1920\n",
       "heir                 1923\n",
       "house                 427\n",
       "spouse               1670\n",
       "book1                   0\n",
       "book2                   0\n",
       "book3                   0\n",
       "book4                   0\n",
       "book5                   0\n",
       "isAliveMother        1925\n",
       "isAliveFather        1920\n",
       "isAliveHeir          1923\n",
       "isAliveSpouse        1670\n",
       "isMarried               0\n",
       "isNoble                 0\n",
       "age                  1513\n",
       "numDeadRelations        0\n",
       "boolDeadRelations       0\n",
       "isPopular               0\n",
       "popularity              0\n",
       "isAlive                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a23f1c48",
   "metadata": {},
   "source": [
    "В выборке 1946 персонажей и 33 исходных признака. Часть столбцов заполнена полностью, но сведения о родственниках, супруге, возрасте и происхождении часто отсутствуют — это нужно учесть при отборе признаков и предобработке."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca61b97a",
   "metadata": {},
   "source": [
    "# Целевая переменная\n",
    "\n",
    "Создадим столбец `death`, показывающий, умер ли персонаж в книгах. Это и будет целевой переменной."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4c966bb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "death\n",
       "0    74.563207\n",
       "1    25.436793\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['death'] = 1 - df['actual']\n",
    "\n",
    "df['death'].value_counts(normalize=True) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d14e3b7",
   "metadata": {},
   "source": [
    "Около 25% персонажей относятся к классу «умер», поэтому классы несбалансированы. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d0008e5",
   "metadata": {},
   "source": [
    "Проверим преобразование целевой переменной на примере Джона Сноу."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "92fdea76",
   "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>name</th>\n",
       "      <th>actual</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1749</th>\n",
       "      <td>Jon Snow</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          name  actual  death\n",
       "1749  Jon Snow       1      0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\n",
    "    df['name'] == 'Jon Snow', ['name', 'actual', 'death']\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "86892ad4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df['death'].value_counts().plot(kind='bar')\n",
    "\n",
    "plt.title('Распределение таргета')\n",
    "plt.xticks([0, 1], ['Жив', 'Мертв'])\n",
    "plt.xlabel('Статус')\n",
    "plt.ylabel('Количество персонажей')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da52625f",
   "metadata": {},
   "source": [
    "# Отбор признаков"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f55720e",
   "metadata": {},
   "source": [
    "Не используем признаки, напрямую раскрывающие исход (`actual`, `isAlive`, `DateoFdeath`) или уже содержащие прогноз другой модели (`pred`, `alive`, `plod`). Также исключим индикаторы `book1`–`book5`, чтобы модель не опиралась на появление персонажа в конкретной части истории."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7566c97f",
   "metadata": {},
   "source": [
    "Посмотрим на долю пропусков по столбцам. Признаки, в которых отсутствует больше 80% значений, использовать не будем."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1c50ac39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isAliveMother        0.989209\n",
       "mother               0.989209\n",
       "isAliveHeir          0.988181\n",
       "heir                 0.988181\n",
       "father               0.986639\n",
       "isAliveFather        0.986639\n",
       "isAliveSpouse        0.858171\n",
       "spouse               0.858171\n",
       "dateOfBirth          0.777492\n",
       "age                  0.777492\n",
       "DateoFdeath          0.771840\n",
       "culture              0.652107\n",
       "title                0.517986\n",
       "house                0.219424\n",
       "actual               0.000000\n",
       "S.No                 0.000000\n",
       "plod                 0.000000\n",
       "male                 0.000000\n",
       "book2                0.000000\n",
       "book1                0.000000\n",
       "alive                0.000000\n",
       "pred                 0.000000\n",
       "name                 0.000000\n",
       "book5                0.000000\n",
       "book3                0.000000\n",
       "book4                0.000000\n",
       "isMarried            0.000000\n",
       "isNoble              0.000000\n",
       "numDeadRelations     0.000000\n",
       "boolDeadRelations    0.000000\n",
       "isPopular            0.000000\n",
       "popularity           0.000000\n",
       "isAlive              0.000000\n",
       "death                0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "934a842a",
   "metadata": {},
   "source": [
    "В итоге выберем следующие признаки:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7a6474aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [\n",
    "    \"title\",\n",
    "    \"male\",\n",
    "    \"culture\",\n",
    "    \"house\",\n",
    "    \"isMarried\",\n",
    "    \"isNoble\",\n",
    "    \"age\",\n",
    "    \"numDeadRelations\",\n",
    "    \"popularity\"\n",
    "]\n",
    "\n",
    "X = df[features].copy()\n",
    "y = df[\"death\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b8774348",
   "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>title</th>\n",
       "      <th>male</th>\n",
       "      <th>culture</th>\n",
       "      <th>house</th>\n",
       "      <th>isMarried</th>\n",
       "      <th>isNoble</th>\n",
       "      <th>age</th>\n",
       "      <th>numDeadRelations</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>0.605351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lord of the Crossing</td>\n",
       "      <td>1</td>\n",
       "      <td>Rivermen</td>\n",
       "      <td>House Frey</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.896321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ser</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Swyft</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.267559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Queen</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Arryn</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.183946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Greenstone</td>\n",
       "      <td>0</td>\n",
       "      <td>Dornish</td>\n",
       "      <td>House Santagar</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  title  male   culture           house  isMarried  isNoble  \\\n",
       "0                   NaN     1       NaN             NaN          0        0   \n",
       "1  Lord of the Crossing     1  Rivermen      House Frey          1        1   \n",
       "2                   Ser     1       NaN     House Swyft          0        1   \n",
       "3                 Queen     0       NaN     House Arryn          1        1   \n",
       "4            Greenstone     0   Dornish  House Santagar          1        1   \n",
       "\n",
       "    age  numDeadRelations  popularity  \n",
       "0   NaN                11    0.605351  \n",
       "1  97.0                 1    0.896321  \n",
       "2   NaN                 0    0.267559  \n",
       "3  23.0                 0    0.183946  \n",
       "4  29.0                 0    0.043478  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "209ab424",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title               1008\n",
       "male                   0\n",
       "culture             1269\n",
       "house                427\n",
       "isMarried              0\n",
       "isNoble                0\n",
       "age                 1513\n",
       "numDeadRelations       0\n",
       "popularity             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec7a052d",
   "metadata": {},
   "source": [
    "В `title`, `culture`, `house` и особенно `age` остаются пропуски. Их обработаем внутри пайплайна, чтобы правила заполнения рассчитывались только по обучающей выборке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3ecbf66c",
   "metadata": {},
   "outputs": [],
   "source": [
    "name = df['name'].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "019dc278",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "008d5771",
   "metadata": {},
   "source": [
    "### Самые смертные дома\n",
    "\n",
    "Сгруппируем персонажей по домам и оставим группы более чем из 10 персонажей: для совсем маленьких домов выводы были бы слишком нестабильными. Здесь сравнивается абсолютное число смертей, а не вероятность смерти внутри дома."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5728f31e",
   "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>characters</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>house</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alchemists' Guild</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antler Men</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Band of Nine</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black Ears</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blacks</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   characters  deaths\n",
       "house                                \n",
       "Alchemists' Guild           7       1\n",
       "Antler Men                  1       0\n",
       "Band of Nine                1       1\n",
       "Black Ears                  2       0\n",
       "Blacks                      4       4"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_stats = (df.dropna(subset=['house'])\n",
    "               .groupby('house')\n",
    "               .agg(characters=('name', 'count'),\n",
    "                    deaths=('death', 'sum')))\n",
    "house_stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3169c705",
   "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>characters</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>house</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Night's Watch</th>\n",
       "      <td>105</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>House Frey</th>\n",
       "      <td>97</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>House Stark</th>\n",
       "      <td>72</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>House Targaryen</th>\n",
       "      <td>62</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>House Lannister</th>\n",
       "      <td>49</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>House Greyjoy</th>\n",
       "      <td>41</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>House Tyrell</th>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>House Martell</th>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>House Osgrey</th>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Faith of the Seven</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    characters  deaths\n",
       "house                                 \n",
       "Night's Watch              105      46\n",
       "House Frey                  97      14\n",
       "House Stark                 72      22\n",
       "House Targaryen             62      41\n",
       "House Lannister             49      18\n",
       "House Greyjoy               41      14\n",
       "House Tyrell                36       2\n",
       "House Martell               29       4\n",
       "House Osgrey                21       4\n",
       "Faith of the Seven          17       2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_stats = house_stats[house_stats['characters'] > 10].copy()\n",
    "house_stats.sort_values('characters', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e43a3af6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 6))\n",
    "\n",
    "plt.barh(house_stats.index, house_stats['deaths'])\n",
    "plt.title('Количество умерших персонажей по домам')\n",
    "plt.xlabel('Количество умерших персонажей')\n",
    "plt.ylabel('Дома')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a891ef2",
   "metadata": {},
   "source": [
    "Больше всего смертей в абсолютных числах наблюдается у Ночного Дозора и дома Таргариенов. Однако результат зависит и от размера дома, поэтому его нельзя напрямую трактовать как индивидуальный риск смерти."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aea7ec3",
   "metadata": {},
   "source": [
    "### Зависимость смертности от сюжетной популярности\n",
    "\n",
    "Разделим персонажей на пять примерно равных групп по популярности. Поскольку `death` принимает значения 0 и 1, среднее по группе равно доле умерших персонажей."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1e9fdeab",
   "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>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>popularity_by_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(-0.001, 0.01]</th>\n",
       "      <td>0.091139</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.01, 0.0234]</th>\n",
       "      <td>0.194954</td>\n",
       "      <td>436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.0234, 0.0502]</th>\n",
       "      <td>0.271233</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.0502, 0.117]</th>\n",
       "      <td>0.339779</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0.117, 1.0]</th>\n",
       "      <td>0.391753</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         mean  count\n",
       "popularity_by_group                 \n",
       "(-0.001, 0.01]       0.091139    395\n",
       "(0.01, 0.0234]       0.194954    436\n",
       "(0.0234, 0.0502]     0.271233    365\n",
       "(0.0502, 0.117]      0.339779    362\n",
       "(0.117, 1.0]         0.391753    388"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['popularity_by_group'] = pd.qcut(df['popularity'], q = 5)\n",
    "\n",
    "popularity_stats = (df.groupby('popularity_by_group')['death'].agg(['mean', 'count']))\n",
    "\n",
    "popularity_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "368c450b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 6))\n",
    "\n",
    "plt.plot(range(len(popularity_stats)), popularity_stats['mean'], marker='o')\n",
    "plt.xticks(range(len(popularity_stats)), ['Очень низкая', 'Низкая', 'Средняя', 'Высокая', 'Очень высокая'], rotation=20)\n",
    "\n",
    "plt.title('Среднее количество умерших персонажей по уровням популярности')\n",
    "plt.xlabel('Популярность')\n",
    "plt.ylabel('Среднее количество умерших персонажей')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e02b97c",
   "metadata": {},
   "source": [
    "С ростом популярности доля умерших последовательно увеличивается: примерно с 9% в первой группе до 39% в последней. Значит, популярность может быть полезным признаком для модели."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0362fdf",
   "metadata": {},
   "source": [
    "### Зависимость смертности от количества погибших родственников\n",
    "\n",
    "Для каждого количества погибших родственников посчитаем долю смертей. Размер точки на графике показывает число персонажей в группе."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0babd66a",
   "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>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numDeadRelations</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.230428</td>\n",
       "      <td>1801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.387755</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.416667</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.640000</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.625000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean  count\n",
       "numDeadRelations                 \n",
       "0                 0.230428   1801\n",
       "1                 0.387755     49\n",
       "2                 0.416667     12\n",
       "3                 0.750000      4\n",
       "4                 0.666667     18\n",
       "5                 0.640000     25\n",
       "6                 0.625000      8\n",
       "7                 0.800000     10\n",
       "8                 0.400000      5\n",
       "9                 0.500000      2\n",
       "10                0.800000      5\n",
       "11                1.000000      3\n",
       "12                0.500000      2\n",
       "15                0.500000      2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relations_stats = (df.groupby('numDeadRelations')['death'].agg(['mean', 'count']))\n",
    "\n",
    "relations_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "993eb131",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "\n",
    "plt.scatter(relations_stats.index, relations_stats['mean'], s=relations_stats['count']*3)\n",
    "\n",
    "plt.title('Доля умерших персонажей по количеству умерших родственников')\n",
    "plt.xlabel('Количество умерших родственников')\n",
    "plt.ylabel('Доля умерших персонажей')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f713c2a6",
   "metadata": {},
   "source": [
    "Среди персонажей без погибших родственников умерли около 23%. При наличии таких родственников доля обычно выше, но группы с большими значениями очень малы, поэтому отдельные высокие оценки могут быть случайными."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ed10d83",
   "metadata": {},
   "source": [
    "# Обучение модели\n",
    "\n",
    "Разделим данные на обучающую и тестовую части в пропорции 75/25. Стратификация сохраняет исходную долю умерших персонажей в обеих выборках."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ab9a688c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "428b4d3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1459, 9)\n",
      "(487, 9)\n",
      "death\n",
      "0    74.571624\n",
      "1    25.428376\n",
      "Name: proportion, dtype: float64\n",
      "death\n",
      "0    74.537988\n",
      "1    25.462012\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.value_counts(normalize=True) * 100)\n",
    "print(y_test.value_counts(normalize=True) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e100a379",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Числовые признаки: Index(['male', 'isMarried', 'isNoble', 'age', 'numDeadRelations',\n",
      "       'popularity'],\n",
      "      dtype='str')\n",
      "Категориальные признаки: Index(['title', 'culture', 'house'], dtype='str')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Masha\\AppData\\Local\\Temp\\ipykernel_23368\\2347361538.py:2: Pandas4Warning: For backward compatibility, 'str' dtypes are included by select_dtypes when 'object' dtype is specified. This behavior is deprecated and will be removed in a future version. Explicitly pass 'str' to `include` to select them, or to `exclude` to remove them and silence this warning.\n",
      "See https://pandas.pydata.org/docs/user_guide/migration-3-strings.html#string-migration-select-dtypes for details on how to write code that works with pandas 2 and 3.\n",
      "  categorical_features = X.select_dtypes(include=['object']).columns\n"
     ]
    }
   ],
   "source": [
    "numerical_features = X.select_dtypes(include=['int64', 'float64']).columns\n",
    "categorical_features = X.select_dtypes(include=['object']).columns\n",
    "\n",
    "print(\"Числовые признаки:\", numerical_features)\n",
    "print(\"Категориальные признаки:\", categorical_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19d7b4c2",
   "metadata": {},
   "source": [
    "Для числовых признаков пропуски заполним медианой и выполним масштабирование. Для категориальных — подставим самое частое значение и применим one-hot encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3de0d085",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "\n",
    "numeric_transformer = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy='median')),\n",
    "    ('scaler', StandardScaler())\n",
    "])\n",
    "\n",
    "categorical_transformer = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
    "])\n",
    "\n",
    "preprocessor = ColumnTransformer([\n",
    "    ('num', numeric_transformer, numerical_features),\n",
    "    ('cat', categorical_transformer, categorical_features)\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3018fd5",
   "metadata": {},
   "source": [
    "В качестве базовой интерпретируемой модели используем логистическую регрессию. Параметр `class_weight='balanced'` повышает вес редкого класса умерших персонажей."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d274a430",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = Pipeline([\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('model', LogisticRegression(max_iter = 1000, class_weight='balanced'))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0bef02e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
       ".features .features-container table td {\n",
       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;male&#x27;, &#x27;isMarried&#x27;, &#x27;isNoble&#x27;, &#x27;age&#x27;, &#x27;numDeadRelations&#x27;,\n",
       "       &#x27;popularity&#x27;],\n",
       "      dtype=&#x27;str&#x27;)),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  Index([&#x27;title&#x27;, &#x27;culture&#x27;, &#x27;house&#x27;], dtype=&#x27;str&#x27;))])),\n",
       "                (&#x27;model&#x27;,\n",
       "                 LogisticRegression(class_weight=&#x27;balanced&#x27;, max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('steps',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-steps;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=steps,-list%20of%20tuples\">\n",
       "            steps\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-steps;\">\n",
       "            steps: list of tuples<br><br>List of (name of step, estimator) tuples that are to be chained in<br>sequential order. To be compatible with the scikit-learn API, all steps<br>must define `fit`. All non-last steps must also define `transform`. See<br>:ref:`Combining Estimators &lt;combining_estimators&gt;` for more details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">[(&#x27;preprocessor&#x27;, ...), (&#x27;model&#x27;, ...)]</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transform_input',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-transform_input;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=transform_input,-list%20of%20str%2C%20default%3DNone\">\n",
       "            transform_input\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-transform_input;\">\n",
       "            transform_input: list of str, default=None<br><br>The names of the :term:`metadata` parameters that should be transformed by the<br>pipeline before passing it to the step consuming it.<br><br>This enables transforming some input arguments to ``fit`` (other than ``X``)<br>to be transformed by the steps of the pipeline up to the step which requires<br>them. Requirement is defined via :ref:`metadata routing &lt;metadata_routing&gt;`.<br>For instance, this can be used to pass a validation set through the pipeline.<br><br>You can only set this if metadata routing is enabled, which you<br>can enable using ``sklearn.set_config(enable_metadata_routing=True)``.<br><br>.. versionadded:: 1.6</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('memory',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-memory;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=memory,-str%20or%20object%20with%20the%20joblib.Memory%20interface%2C%20default%3DNone\">\n",
       "            memory\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-memory;\">\n",
       "            memory: str or object with the joblib.Memory interface, default=None<br><br>Used to cache the fitted transformers of the pipeline. The last step<br>will never be cached, even if it is a transformer. By default, no<br>caching is performed. If a string is given, it is the path to the<br>caching directory. Enabling caching triggers a clone of the transformers<br>before fitting. Therefore, the transformer instance given to the<br>pipeline cannot be inspected directly. Use the attribute ``named_steps``<br>or ``steps`` to inspect estimators within the pipeline. Caching the<br>transformers is advantageous when fitting is time consuming. See<br>:ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`<br>for an example on how to enable caching.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: bool, default=False<br><br>If True, the time elapsed while fitting each step will be printed as it<br>is completed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: ndarray of shape (n_classes,)<br><br>The classes labels. Only exist if the last step of the pipeline is a<br>classifier.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
       "           <td>[0,1]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-feature_names_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=feature_names_in_,-ndarray%20of%20shape%20%28n_features_in_%2C%29\">\n",
       "            feature_names_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-feature_names_in_;\">\n",
       "            feature_names_in_: ndarray of shape (`n_features_in_`,)<br><br>Names of features seen during :term:`fit`. Only defined if the<br>underlying estimator exposes such an attribute when fit.<br><br>.. versionadded:: 1.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[object](9,)</td>\n",
       "           <td>[&#x27;title&#x27;,&#x27;male&#x27;,&#x27;culture&#x27;,...,&#x27;age&#x27;,&#x27;numDeadRelations&#x27;,&#x27;popularity&#x27;]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`. Only defined if the<br>underlying first estimator in `steps` exposes such an attribute<br>when fit.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>9</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>preprocessor: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"preprocessor__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transformers',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-transformers;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformers,-list%20of%20tuples\">\n",
       "            transformers\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-transformers;\">\n",
       "            transformers: list of tuples<br><br>List of (name, transformer, columns) tuples specifying the<br>transformer objects to be applied to subsets of the data.<br><br>name : str<br>    Like in Pipeline and FeatureUnion, this allows the transformer and<br>    its parameters to be set using ``set_params`` and searched in grid<br>    search.<br>transformer : {&#x27;drop&#x27;, &#x27;passthrough&#x27;} or estimator<br>    Estimator must support :term:`fit` and :term:`transform`.<br>    Special-cased strings &#x27;drop&#x27; and &#x27;passthrough&#x27; are accepted as<br>    well, to indicate to drop the columns or to pass them through<br>    untransformed, respectively.<br>columns :  str, array-like of str, int, array-like of int,                 array-like of bool, slice or callable<br>    Indexes the data on its second axis. Integers are interpreted as<br>    positional columns, while strings can reference DataFrame columns<br>    by name.  A scalar string or int should be used where<br>    ``transformer`` expects X to be a 1d array-like (vector),<br>    otherwise a 2d array will be passed to the transformer.<br>    A callable is passed the input data `X` and can return any of the<br>    above. To select multiple columns by name or dtype, you can use<br>    :obj:`make_column_selector`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">[(&#x27;num&#x27;, ...), (&#x27;cat&#x27;, ...)]</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('remainder',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-remainder;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=remainder,-%7B%27drop%27%2C%20%27passthrough%27%7D%20or%20estimator%2C%20default%3D%27drop%27\">\n",
       "            remainder\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-remainder;\">\n",
       "            remainder: {&#x27;drop&#x27;, &#x27;passthrough&#x27;} or estimator, default=&#x27;drop&#x27;<br><br>By default, only the specified columns in `transformers` are<br>transformed and combined in the output, and the non-specified<br>columns are dropped. (default of ``&#x27;drop&#x27;``).<br>By specifying ``remainder=&#x27;passthrough&#x27;``, all remaining columns that<br>were not specified in `transformers`, but present in the data passed<br>to `fit` will be automatically passed through. This subset of columns<br>is concatenated with the output of the transformers. For dataframes,<br>extra columns not seen during `fit` will be excluded from the output<br>of `transform`.<br>By setting ``remainder`` to be an estimator, the remaining<br>non-specified columns will use the ``remainder`` estimator. The<br>estimator must support :term:`fit` and :term:`transform`.<br>Note that using this feature requires that the DataFrame columns<br>input at :term:`fit` and :term:`transform` have identical order.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;drop&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sparse_threshold',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-sparse_threshold;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=sparse_threshold,-float%2C%20default%3D0.3\">\n",
       "            sparse_threshold\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-sparse_threshold;\">\n",
       "            sparse_threshold: float, default=0.3<br><br>If the output of the different transformers contains sparse matrices,<br>these will be stacked as a sparse matrix if the overall density is<br>lower than this value. Use ``sparse_threshold=0`` to always return<br>dense.  When the transformed output consists of all dense data, the<br>stacked result will be dense, and this keyword will be ignored.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary &lt;n_jobs&gt;`<br>for more details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transformer_weights',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-transformer_weights;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformer_weights,-dict%2C%20default%3DNone\">\n",
       "            transformer_weights\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-transformer_weights;\">\n",
       "            transformer_weights: dict, default=None<br><br>Multiplicative weights for features per transformer. The output of the<br>transformer is multiplied by these weights. Keys are transformer names,<br>values the weights.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: bool, default=False<br><br>If True, the time elapsed while fitting each transformer will be<br>printed as it is completed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose_feature_names_out',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose_feature_names_out;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose_feature_names_out,-bool%2C%20str%20or%20Callable%5B%5Bstr%2C%20str%5D%2C%20str%5D%2C%20default%3DTrue\">\n",
       "            verbose_feature_names_out\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose_feature_names_out;\">\n",
       "            verbose_feature_names_out: bool, str or Callable[[str, str], str], default=True<br><br>- If True, :meth:`ColumnTransformer.get_feature_names_out` will prefix<br>  all feature names with the name of the transformer that generated that<br>  feature. It is equivalent to setting<br>  `verbose_feature_names_out=&quot;{transformer_name}__{feature_name}&quot;`.<br>- If False, :meth:`ColumnTransformer.get_feature_names_out` will not<br>  prefix any feature names and will error if feature names are not<br>  unique.<br>- If ``Callable[[str, str], str]``,<br>  :meth:`ColumnTransformer.get_feature_names_out` will rename all the features<br>  using the name of the transformer. The first argument of the callable is the<br>  transformer name and the second argument is the feature name. The returned<br>  string will be the new feature name.<br>- If ``str``, it must be a string ready for formatting. The given string will<br>  be formatted using two field names: ``transformer_name`` and ``feature_name``.<br>  e.g. ``&quot;{feature_name}__{transformer_name}&quot;``. See :meth:`str.format` method<br>  from the standard library for more info.<br><br>.. versionadded:: 1.0<br><br>.. versionchanged:: 1.6<br>    `verbose_feature_names_out` can be a callable or a string to be formatted.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-feature_names_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=feature_names_in_,-ndarray%20of%20shape%20%28n_features_in_%2C%29\">\n",
       "            feature_names_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-feature_names_in_;\">\n",
       "            feature_names_in_: ndarray of shape (`n_features_in_`,)<br><br>Names of features seen during :term:`fit`. Defined only when `X`<br>has feature names that are all strings.<br><br>.. versionadded:: 1.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[object](9,)</td>\n",
       "           <td>[&#x27;title&#x27;,&#x27;male&#x27;,&#x27;culture&#x27;,...,&#x27;age&#x27;,&#x27;numDeadRelations&#x27;,&#x27;popularity&#x27;]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`. Only defined if the<br>underlying transformers expose such an attribute when fit.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>9</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-named_transformers_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=named_transformers_,-%3Aclass%3A~sklearn.utils.Bunch\">\n",
       "            named_transformers_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-named_transformers_;\">\n",
       "            named_transformers_: :class:`~sklearn.utils.Bunch`<br><br>Read-only attribute to access any transformer by given name.<br>Keys are transformer names and values are the fitted transformer<br>objects.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">Bunch</td>\n",
       "           <td>{&#x27;num&#x27;: Pipel...=&#x27;ignore&#x27;))])}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-output_indices_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=output_indices_,-dict\">\n",
       "            output_indices_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-output_indices_;\">\n",
       "            output_indices_: dict<br><br>A dictionary from each transformer name to a slice, where the slice<br>corresponds to indices in the transformed output. This is useful to<br>inspect which transformer is responsible for which transformed<br>feature(s).<br><br>.. versionadded:: 1.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{&#x27;cat&#x27;: slice(6, 575, None), &#x27;num&#x27;: slice(0, 6, None), &#x27;re...er&#x27;: slice(0, 0, None)}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-sparse_output_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=sparse_output_,-bool\">\n",
       "            sparse_output_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-sparse_output_;\">\n",
       "            sparse_output_: bool<br><br>Boolean flag indicating whether the output of ``transform`` is a<br>sparse matrix or a dense numpy array, which depends on the output<br>of the individual transformers and the `sparse_threshold` keyword.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">bool</td>\n",
       "           <td>True</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-transformers_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformers_,-list\">\n",
       "            transformers_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-transformers_;\">\n",
       "            transformers_: list<br><br>The collection of fitted transformers as tuples of (name,<br>fitted_transformer, column). `fitted_transformer` can be an estimator,<br>or `&#x27;drop&#x27;`; `&#x27;passthrough&#x27;` is replaced with an equivalent<br>:class:`~sklearn.preprocessing.FunctionTransformer`. In case there were<br>no columns selected, this will be the unfitted transformer. If there<br>are remaining columns, the final element is a tuple of the form:<br>(&#x27;remainder&#x27;, transformer, remaining_columns) corresponding to the<br>``remainder`` parameter. If there are remaining columns, then<br>``len(transformers_)==len(transformers)+1``, otherwise<br>``len(transformers_)==len(transformers)``.<br><br>.. versionadded:: 1.7<br>    The format of the remaining columns now attempts to match that of the other<br>    transformers: if all columns were provided as column names (`str`), the<br>    remaining columns are stored as column names; if all columns were provided<br>    as mask arrays (`bool`), so are the remaining columns; in all other cases<br>    the remaining columns are stored as indices (`int`).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">list</td>\n",
       "           <td>[(&#x27;num&#x27;, Pipeline(step...ardScaler())]), Index([&#x27;male&#x27;...  dtype=&#x27;str&#x27;)), (&#x27;cat&#x27;, Pipeline(step...n=&#x27;ignore&#x27;))]), Index([&#x27;title..., dtype=&#x27;str&#x27;))]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>num</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"preprocessor__num__\"><pre>Index([&#x27;male&#x27;, &#x27;isMarried&#x27;, &#x27;isNoble&#x27;, &#x27;age&#x27;, &#x27;numDeadRelations&#x27;,\n",
       "       &#x27;popularity&#x27;],\n",
       "      dtype=&#x27;str&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"preprocessor__num__imputer__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('strategy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-strategy;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
       "            strategy\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-strategy;\">\n",
       "            strategy: str or Callable, default=&#x27;mean&#x27;<br><br>The imputation strategy.<br><br>- If &quot;mean&quot;, then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If &quot;median&quot;, then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If &quot;most_frequent&quot;, then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If &quot;constant&quot;, then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=&quot;constant&quot; for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('missing_values',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-missing_values;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
       "            missing_values\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-missing_values;\">\n",
       "            missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas&#x27; dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">nan</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fill_value',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-fill_value;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
       "            fill_value\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-fill_value;\">\n",
       "            fill_value: str or numerical value, default=None<br><br>When strategy == &quot;constant&quot;, `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and &quot;missing_value&quot; for strings or object data types.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-copy;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-copy;\">\n",
       "            copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('add_indicator',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-add_indicator;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
       "            add_indicator\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-add_indicator;\">\n",
       "            add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer&#x27;s transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won&#x27;t appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-keep_empty_features;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
       "            keep_empty_features\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-keep_empty_features;\">\n",
       "            keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=&quot;constant&quot;`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-feature_names_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=feature_names_in_,-ndarray%20of%20shape%20%28n_features_in_%2C%29\">\n",
       "            feature_names_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-feature_names_in_;\">\n",
       "            feature_names_in_: ndarray of shape (`n_features_in_`,)<br><br>Names of features seen during :term:`fit`. Defined only when `X`<br>has feature names that are all strings.<br><br>.. versionadded:: 1.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[object](6,)</td>\n",
       "           <td>[&#x27;male&#x27;,&#x27;isMarried&#x27;,&#x27;isNoble&#x27;,&#x27;age&#x27;,&#x27;numDeadRelations&#x27;,&#x27;popularity&#x27;]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-indicator_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=indicator_,-%3Aclass%3A~sklearn.impute.MissingIndicator\">\n",
       "            indicator_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-indicator_;\">\n",
       "            indicator_: :class:`~sklearn.impute.MissingIndicator`<br><br>Indicator used to add binary indicators for missing values.<br>`None` if `add_indicator=False`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">NoneType</td>\n",
       "           <td>None</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>6</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-statistics_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=statistics_,-array%20of%20shape%20%28n_features%2C%29\">\n",
       "            statistics_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-statistics_;\">\n",
       "            statistics_: array of shape (n_features,)<br><br>The imputation fill value for each feature.<br>Computing statistics can result in `np.nan` values.<br>During :meth:`transform`, features corresponding to `np.nan`<br>statistics will be discarded.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](6,)</td>\n",
       "           <td>[ 1.  , 0.  , 0.  ,28.5 , 0.  , 0.03]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div>\n",
       "        <div class=\"features fitted\">\n",
       "          <details>\n",
       "            <summary>\n",
       "              <div class=\"arrow\"></div>\n",
       "              <div>6 features</div>\n",
       "              <div class=\"image-container\" title=\"Copy all output features\">\n",
       "                <i class=\"copy-paste-icon\"\n",
       "                  onclick=\"\n",
       "                  event.stopPropagation();\n",
       "                  event.preventDefault();\n",
       "                  copyFeatureNamesToClipboard(this);\n",
       "                  \"\n",
       "                >\n",
       "                </i>\n",
       "              </div>\n",
       "            </summary>\n",
       "            <div class=\"features-container\">\n",
       "                <table class=\"features-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr>\n",
       "          <td>male</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>isMarried</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>isNoble</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>age</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>numDeadRelations</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>popularity</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </div>\n",
       "          </details>\n",
       "        </div>\n",
       "    <div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"preprocessor__num__scaler__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-copy;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-copy;\">\n",
       "            copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_mean',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-with_mean;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
       "            with_mean\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-with_mean;\">\n",
       "            with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_std',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-with_std;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
       "            with_std\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-with_std;\">\n",
       "            with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-mean_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=mean_,-ndarray%20of%20shape%20%28n_features%2C%29%20or%20None\">\n",
       "            mean_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-mean_;\">\n",
       "            mean_: ndarray of shape (n_features,) or None<br><br>The mean value for each feature in the training set.<br>Equal to ``None`` when ``with_mean=False`` and ``with_std=False``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](6,)</td>\n",
       "           <td>[   0.63,   0.14,   0.46,-173.77,   0.3 ,   0.09]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>6</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_samples_seen_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=n_samples_seen_,-int%20or%20ndarray%20of%20shape%20%28n_features%2C%29\">\n",
       "            n_samples_seen_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_samples_seen_;\">\n",
       "            n_samples_seen_: int or ndarray of shape (n_features,)<br><br>The number of samples processed by the estimator for each feature.<br>If there are no missing samples, the ``n_samples_seen`` will be an<br>integer, otherwise it will be an array of dtype int. If<br>`sample_weights` are used it will be a float (if no missing data)<br>or an array of dtype float that sums the weights seen so far.<br>Will be reset on new calls to fit, but increments across<br>``partial_fit`` calls.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">float64</td>\n",
       "           <td>1459</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-scale_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=scale_,-ndarray%20of%20shape%20%28n_features%2C%29%20or%20None\">\n",
       "            scale_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-scale_;\">\n",
       "            scale_: ndarray of shape (n_features,) or None<br><br>Per feature relative scaling of the data to achieve zero mean and unit<br>variance. Generally this is calculated using `np.sqrt(var_)`. If a<br>variance is zero, we can&#x27;t achieve unit variance, and the data is left<br>as-is, giving a scaling factor of 1. `scale_` is equal to `None`<br>when `with_std=False`.<br><br>.. versionadded:: 0.17<br>   *scale_*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](6,)</td>\n",
       "           <td>[   0.48,   0.35,   0.5 ,7799.85,   1.32,   0.16]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-var_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=var_,-ndarray%20of%20shape%20%28n_features%2C%29%20or%20None\">\n",
       "            var_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-var_;\">\n",
       "            var_: ndarray of shape (n_features,) or None<br><br>The variance for each feature in the training set. Used to compute<br>`scale_`. Equal to ``None`` when ``with_mean=False`` and<br>``with_std=False``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](6,)</td>\n",
       "           <td>[       0.23,       0.12,       0.25,60837648.22,       1.74,       0.03]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div>\n",
       "        <div class=\"features fitted\">\n",
       "          <details>\n",
       "            <summary>\n",
       "              <div class=\"arrow\"></div>\n",
       "              <div>6 features</div>\n",
       "              <div class=\"image-container\" title=\"Copy all output features\">\n",
       "                <i class=\"copy-paste-icon\"\n",
       "                  onclick=\"\n",
       "                  event.stopPropagation();\n",
       "                  event.preventDefault();\n",
       "                  copyFeatureNamesToClipboard(this);\n",
       "                  \"\n",
       "                >\n",
       "                </i>\n",
       "              </div>\n",
       "            </summary>\n",
       "            <div class=\"features-container\">\n",
       "                <table class=\"features-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr>\n",
       "          <td>x0</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x4</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x5</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </div>\n",
       "          </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>cat</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"preprocessor__cat__\"><pre>Index([&#x27;title&#x27;, &#x27;culture&#x27;, &#x27;house&#x27;], dtype=&#x27;str&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"preprocessor__cat__imputer__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('strategy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-strategy;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
       "            strategy\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-strategy;\">\n",
       "            strategy: str or Callable, default=&#x27;mean&#x27;<br><br>The imputation strategy.<br><br>- If &quot;mean&quot;, then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If &quot;median&quot;, then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If &quot;most_frequent&quot;, then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If &quot;constant&quot;, then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=&quot;constant&quot; for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;most_frequent&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('missing_values',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-missing_values;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
       "            missing_values\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-missing_values;\">\n",
       "            missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas&#x27; dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">nan</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fill_value',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-fill_value;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
       "            fill_value\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-fill_value;\">\n",
       "            fill_value: str or numerical value, default=None<br><br>When strategy == &quot;constant&quot;, `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and &quot;missing_value&quot; for strings or object data types.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-copy;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-copy;\">\n",
       "            copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('add_indicator',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-add_indicator;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
       "            add_indicator\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-add_indicator;\">\n",
       "            add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer&#x27;s transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won&#x27;t appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-keep_empty_features;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
       "            keep_empty_features\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-keep_empty_features;\">\n",
       "            keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=&quot;constant&quot;`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-feature_names_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=feature_names_in_,-ndarray%20of%20shape%20%28n_features_in_%2C%29\">\n",
       "            feature_names_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-feature_names_in_;\">\n",
       "            feature_names_in_: ndarray of shape (`n_features_in_`,)<br><br>Names of features seen during :term:`fit`. Defined only when `X`<br>has feature names that are all strings.<br><br>.. versionadded:: 1.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[object](3,)</td>\n",
       "           <td>[&#x27;title&#x27;,&#x27;culture&#x27;,&#x27;house&#x27;]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-indicator_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=indicator_,-%3Aclass%3A~sklearn.impute.MissingIndicator\">\n",
       "            indicator_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-indicator_;\">\n",
       "            indicator_: :class:`~sklearn.impute.MissingIndicator`<br><br>Indicator used to add binary indicators for missing values.<br>`None` if `add_indicator=False`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">NoneType</td>\n",
       "           <td>None</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>3</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-statistics_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=statistics_,-array%20of%20shape%20%28n_features%2C%29\">\n",
       "            statistics_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-statistics_;\">\n",
       "            statistics_: array of shape (n_features,)<br><br>The imputation fill value for each feature.<br>Computing statistics can result in `np.nan` values.<br>During :meth:`transform`, features corresponding to `np.nan`<br>statistics will be discarded.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[object](3,)</td>\n",
       "           <td>[&#x27;Ser&#x27;,&#x27;Northmen&#x27;,&quot;Night&#x27;s Watch&quot;]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div>\n",
       "        <div class=\"features fitted\">\n",
       "          <details>\n",
       "            <summary>\n",
       "              <div class=\"arrow\"></div>\n",
       "              <div>3 features</div>\n",
       "              <div class=\"image-container\" title=\"Copy all output features\">\n",
       "                <i class=\"copy-paste-icon\"\n",
       "                  onclick=\"\n",
       "                  event.stopPropagation();\n",
       "                  event.preventDefault();\n",
       "                  copyFeatureNamesToClipboard(this);\n",
       "                  \"\n",
       "                >\n",
       "                </i>\n",
       "              </div>\n",
       "            </summary>\n",
       "            <div class=\"features-container\">\n",
       "                <table class=\"features-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr>\n",
       "          <td>title</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>culture</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>house</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </div>\n",
       "          </details>\n",
       "        </div>\n",
       "    <div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"preprocessor__cat__onehot__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('handle_unknown',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-handle_unknown;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=handle_unknown,-%7B%27error%27%2C%20%27ignore%27%2C%20%27infrequent_if_exist%27%2C%20%27warn%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27error%27\">\n",
       "            handle_unknown\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-handle_unknown;\">\n",
       "            handle_unknown: {&#x27;error&#x27;, &#x27;ignore&#x27;, &#x27;infrequent_if_exist&#x27;, &#x27;warn&#x27;},                      default=&#x27;error&#x27;<br><br>Specifies the way unknown categories are handled during :meth:`transform`.<br><br>- &#x27;error&#x27; : Raise an error if an unknown category is present during transform.<br>- &#x27;ignore&#x27; : When an unknown category is encountered during<br>  transform, the resulting one-hot encoded columns for this feature<br>  will be all zeros. In the inverse transform, an unknown category<br>  will be denoted as None.<br>- &#x27;infrequent_if_exist&#x27; : When an unknown category is encountered<br>  during transform, the resulting one-hot encoded columns for this<br>  feature will map to the infrequent category if it exists. The<br>  infrequent category will be mapped to the last position in the<br>  encoding. During inverse transform, an unknown category will be<br>  mapped to the category denoted `&#x27;infrequent&#x27;` if it exists. If the<br>  `&#x27;infrequent&#x27;` category does not exist, then :meth:`transform` and<br>  :meth:`inverse_transform` will handle an unknown category as with<br>  `handle_unknown=&#x27;ignore&#x27;`. Infrequent categories exist based on<br>  `min_frequency` and `max_categories`. Read more in the<br>  :ref:`User Guide &lt;encoder_infrequent_categories&gt;`.<br>- &#x27;warn&#x27; : When an unknown category is encountered during transform<br>  a warning is issued, and the encoding then proceeds as described for<br>  `handle_unknown=&quot;infrequent_if_exist&quot;`.<br><br>.. versionchanged:: 1.1<br>    `&#x27;infrequent_if_exist&#x27;` was added to automatically handle unknown<br>    categories and infrequent categories.<br><br>.. versionadded:: 1.6<br>   The option `&quot;warn&quot;` was added in 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;ignore&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('categories',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-categories;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=categories,-%27auto%27%20or%20a%20list%20of%20array-like%2C%20default%3D%27auto%27\">\n",
       "            categories\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-categories;\">\n",
       "            categories: &#x27;auto&#x27; or a list of array-like, default=&#x27;auto&#x27;<br><br>Categories (unique values) per feature:<br><br>- &#x27;auto&#x27; : Determine categories automatically from the training data.<br>- list : ``categories[i]`` holds the categories expected in the ith<br>  column. The passed categories should not mix strings and numeric<br>  values within a single feature, and should be sorted in case of<br>  numeric values.<br><br>The used categories can be found in the ``categories_`` attribute.<br><br>.. versionadded:: 0.20</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('drop',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-drop;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=drop,-%7B%27first%27%2C%20%27if_binary%27%7D%20or%20an%20array-like%20of%20shape%20%28n_features%2C%29%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
       "            drop\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-drop;\">\n",
       "            drop: {&#x27;first&#x27;, &#x27;if_binary&#x27;} or an array-like of shape (n_features,),             default=None<br><br>Specifies a methodology to use to drop one of the categories per<br>feature. This is useful in situations where perfectly collinear<br>features cause problems, such as when feeding the resulting data<br>into an unregularized linear regression model.<br><br>However, dropping one category breaks the symmetry of the original<br>representation and can therefore induce a bias in downstream models,<br>for instance for penalized linear classification or regression models.<br><br>- None : retain all features (the default).<br>- &#x27;first&#x27; : drop the first category in each feature. If only one<br>  category is present, the feature will be dropped entirely.<br>- &#x27;if_binary&#x27; : drop the first category in each feature with two<br>  categories. Features with 1 or more than 2 categories are<br>  left intact.<br>- array : ``drop[i]`` is the category in feature ``X[:, i]`` that<br>  should be dropped.<br><br>When `max_categories` or `min_frequency` is configured to group<br>infrequent categories, the dropping behavior is handled after the<br>grouping.<br><br>.. versionadded:: 0.21<br>   The parameter `drop` was added in 0.21.<br><br>.. versionchanged:: 0.23<br>   The option `drop=&#x27;if_binary&#x27;` was added in 0.23.<br><br>.. versionchanged:: 1.1<br>    Support for dropping infrequent categories.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sparse_output',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-sparse_output;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=sparse_output,-bool%2C%20default%3DTrue\">\n",
       "            sparse_output\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-sparse_output;\">\n",
       "            sparse_output: bool, default=True<br><br>When ``True``, it returns a SciPy sparse matrix/array<br>in &quot;Compressed Sparse Row&quot; (CSR) format.<br><br>.. versionadded:: 1.2<br>   `sparse` was renamed to `sparse_output`</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dtype',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-dtype;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=dtype,-number%20type%2C%20default%3Dnp.float64\">\n",
       "            dtype\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-dtype;\">\n",
       "            dtype: number type, default=np.float64<br><br>Desired dtype of output.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&lt;class &#x27;numpy.float64&#x27;&gt;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_frequency',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-min_frequency;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=min_frequency,-int%20or%20float%2C%20default%3DNone\">\n",
       "            min_frequency\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-min_frequency;\">\n",
       "            min_frequency: int or float, default=None<br><br>Specifies the minimum frequency below which a category will be<br>considered infrequent.<br><br>- If `int`, categories with a smaller cardinality will be considered<br>  infrequent.<br><br>- If `float`, categories with a smaller cardinality than<br>  `min_frequency * n_samples`  will be considered infrequent.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide &lt;encoder_infrequent_categories&gt;`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_categories',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_categories;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=max_categories,-int%2C%20default%3DNone\">\n",
       "            max_categories\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_categories;\">\n",
       "            max_categories: int, default=None<br><br>Specifies an upper limit to the number of output features for each input<br>feature when considering infrequent categories. If there are infrequent<br>categories, `max_categories` includes the category representing the<br>infrequent categories along with the frequent categories. If `None`,<br>there is no limit to the number of output features.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide &lt;encoder_infrequent_categories&gt;`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('feature_name_combiner',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-feature_name_combiner;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=feature_name_combiner,-%22concat%22%20or%20callable%2C%20default%3D%22concat%22\">\n",
       "            feature_name_combiner\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-feature_name_combiner;\">\n",
       "            feature_name_combiner: &quot;concat&quot; or callable, default=&quot;concat&quot;<br><br>Callable with signature `def callable(input_feature, category)` that returns a<br>string. This is used to create feature names to be returned by<br>:meth:`get_feature_names_out`.<br><br>`&quot;concat&quot;` concatenates encoded feature name and category with<br>`feature + &quot;_&quot; + str(category)`.E.g. feature X with values 1, 6, 7 create<br>feature names `X_1, X_6, X_7`.<br><br>.. versionadded:: 1.3</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;concat&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-categories_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=categories_,-list%20of%20arrays\">\n",
       "            categories_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-categories_;\">\n",
       "            categories_: list of arrays<br><br>The categories of each feature determined during fitting<br>(in order of the features in X and corresponding with the output<br>of ``transform``). This includes the category specified in ``drop``<br>(if any).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">list</td>\n",
       "           <td>[array([&#x27;Acorn... dtype=object), array([&#x27;Andal... dtype=object), array([&quot;Alche... dtype=object)]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-drop_idx_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=drop_idx_,-array%20of%20shape%20%28n_features%2C%29\">\n",
       "            drop_idx_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-drop_idx_;\">\n",
       "            drop_idx_: array of shape (n_features,)<br><br>- ``drop_idx_[i]`` is the index in ``categories_[i]`` of the category<br>  to be dropped for each feature.<br>- ``drop_idx_[i] = None`` if no category is to be dropped from the<br>  feature with index ``i``, e.g. when `drop=&#x27;if_binary&#x27;` and the<br>  feature isn&#x27;t binary.<br>- ``drop_idx_ = None`` if all the transformed features will be<br>  retained.<br><br>If infrequent categories are enabled by setting `min_frequency` or<br>`max_categories` to a non-default value and `drop_idx[i]` corresponds<br>to an infrequent category, then the entire infrequent category is<br>dropped.<br><br>.. versionchanged:: 0.23<br>   Added the possibility to contain `None` values.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">NoneType</td>\n",
       "           <td>None</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 1.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>3</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div>\n",
       "        <div class=\"features fitted\">\n",
       "          <details>\n",
       "            <summary>\n",
       "              <div class=\"arrow\"></div>\n",
       "              <div>569 features</div>\n",
       "              <div class=\"image-container\" title=\"Copy all output features\">\n",
       "                <i class=\"copy-paste-icon\"\n",
       "                  onclick=\"\n",
       "                  event.stopPropagation();\n",
       "                  event.preventDefault();\n",
       "                  copyFeatureNamesToClipboard(this);\n",
       "                  \"\n",
       "                >\n",
       "                </i>\n",
       "              </div>\n",
       "            </summary>\n",
       "            <div class=\"features-container\">\n",
       "                <table class=\"features-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr>\n",
       "          <td>x0_Acorn Hall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Andals</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Arbor</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Archmaester</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Ashford</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Barrowton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Bear Island</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Big BucketThe Wull</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Bitterbridge</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Blackcrown</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Blackmont</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Bloodrider</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Blue Grace</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Brightwater</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Broad Arch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Brother</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Captain</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Captain of the guard</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Captain-General</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Castellan</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_CastellanCommander</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Casterly Rock</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Castle Lychester</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Cerwyn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Cobblecat</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Coldmoat</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Coldwater Burn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Commander of the City Watch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Commander of the Second Sons</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Crag</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Crakehall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Cupbearer</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Darry</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Deepwood Motte</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Dreadfort</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Duskendale</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Dyre Den</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Eastwatch-by-the-Sea</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Eyrie</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Fair Isle</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Feastfires</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Felwood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_First Ranger</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_First Sword of Braavos</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Foamdrinker</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Ghost Hill</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Godswife</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Golden Tooth</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Goldengrove</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Goldgrass</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Good Master</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Goodman</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Goodwife</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Grand Maester</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Grassy Vale</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Greenshield</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Greenstone</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Grey Glen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Greywater Watch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Gulltown</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Hand of the King</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Harlaw</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Harrenhal</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Harridan Hill</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Hayford</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Haystack Hall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Heart&#x27;s Home</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_High Septon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_High Steward of Highgarden</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Highgarden</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Hightower</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Horn Hill</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Hornvale</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Hornwood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Ironoaks</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Karhold</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Kayce</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Keeper of the Gates of the Moon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Khal</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_KhalKo (formerly)</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Khalakka</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_King</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_King in the North</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_King of Winter</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_King of the Andals</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_King-Beyond-the-Wall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Knight</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Knight of Griffin&#x27;s Roost</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lady</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lady of Bear Island</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lady of Darry</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lady of Torrhen&#x27;s Square</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lady of the Vale</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_LadyQueen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_LadyQueenDowager Queen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Last Hearth</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Light of the West</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lonely Light</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Longsister</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Longtable</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord Captain of the Iron Fleet</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord Commander of the Night&#x27;s Watch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord Paramount of the Mander</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord Paramount of the Trident</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord Reaper of Pyke</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord Seneschal</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord Steward</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of Blackhaven</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of Coldmoat</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of Crows Nest</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of Dragonstone</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of Flint&#x27;s Finger</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of Greyshield</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of Griffin&#x27;s Roost</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of Hammerhorn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of Harrenhal</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of Oakenshield</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of Oldcastle</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of Pebbleton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of Starfall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of Sunflower Hall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of the Crossing</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of the Deep Den</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of the Hornwood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of the Iron Islands</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of the Marches</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of the Red Dunes</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of the Seven Kingdoms</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of the Snakewood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of the Ten TowersLord Harlaw of HarlawHarlaw of Harlaw</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Lord of the Tides</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Maester</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Magister</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Maidenpool</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Master of Coin</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Master of Deepwood Motte</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Master of Harlaw Hall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Master of coin</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Master of whisperers</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Master-at-Arms</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Mistress of whisperers</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Nightsong</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Oarmaster</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Old Oak</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Old Wyk</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Prince</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Prince of Dorne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Prince of Dragonstone</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Prince of Winterfell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Prince of WinterfellHeir to Winterfell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Prince of the Narrow Sea</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Princess</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_PrincessQueen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_PrincessQueenDowager Queen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Protector of the Realm</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Queen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_QueenDowager Queen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Red Flower Vale</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Redfort</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Rills</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Riverrun</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Rook&#x27;s Rest</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Ruddy Hall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Runestone</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Salt Shore</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Sandship</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Sealord</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Sealskin Point</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Seneschal</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Septa</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Septon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Ser</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_SerCastellan of Casterly Rock</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Seven Kingdoms</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Sharp Point</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Shatterstone</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Slave of R&#x27;hllor</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Starpike</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Steward</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Stokeworth</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Stonehelm</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Storm&#x27;s End</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Sunspear</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Ten Towers</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_The LiddleLord Liddle</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_The NorreyLord Norrey</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Three Towers</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Tower of Glimmering</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Tradesman-Captain</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Twins</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Uplands</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Volmark</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Warlock</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Whitewalls</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Widow&#x27;s Watch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Winterfell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Wisdom</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_Wraith</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_[1]</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_green lands</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_master of ships</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_red hand</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_the Crossing</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x0_the Dreadfort</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Andal</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Andals</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Asshai</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Asshai&#x27;i</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Astapor</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Astapori</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Braavosi</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Crannogmen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Dorne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Dornish</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Dornishmen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Dothraki</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_First Men</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Free Folk</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Free folk</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Ghiscari</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Ghiscaricari</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Ironborn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Ironmen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Lhazareen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Lhazarene</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Lysene</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Lyseni</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Meereen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Meereenese</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Myrish</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Northern mountain clans</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Northmen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Norvos</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Norvoshi</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Pentoshi</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Qarth</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Qartheen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Qohor</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Reach</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Reachmen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Rhoynar</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Rivermen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Sistermen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Stormlander</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Stormlands</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Summer Islander</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Summer Islands</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Summer Isles</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Tyroshi</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Vale</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Vale mountain clans</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Valemen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Valyrian</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Westerlands</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Westerman</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Westermen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Westeros</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Wildling</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_Wildlings</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_free folk</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_northmen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x1_westermen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Alchemists&#x27; Guild</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Antler Men</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Band of Nine</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Black Ears</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Blacks</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Brave Companions</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Brotherhood Without Banners</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Brotherhood without Banners</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Brotherhood without banners</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Burned Men</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Chataya&#x27;s brothel</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Citadel</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Company of the Cat</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Drowned men</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Faceless Men</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Faith of the Seven</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Golden Company</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Good Masters</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Happy Port</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Ambrose</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Arryn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Ashford</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Baelish</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Ball</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Bar Emmon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Baratheon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Baratheon of Dragonstone</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Baratheon of King&#x27;s Landing</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Beesbury</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Belmore</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Bettley</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Blackberry</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Blackfyre</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Blackmont</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Blackwood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Boggs</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Bolling</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Bolton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Bolton of the Dreadfort</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Botley</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Bracken</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Brax</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Broom</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Brune of Brownhollow</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Brune of the Dyre Den</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Buckler</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Bulwer</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Bushy</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Butterwell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Byrch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Bywater</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Cafferen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Caron</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Cassel</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Caswell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Cerwyn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Charlton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Chelsted</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Chester</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Clegane</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Clifton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Cockshaw</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Codd</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Coldwater</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Condon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Connington</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Corbray</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Costayne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Crabb</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Crakehall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Crane</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Cupps</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Cuy</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Dalt</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Darklyn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Darry</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Dayne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Dayne of High Hermitage</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Deddings</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Dondarrion</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Drumm</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Dustin</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Erenford</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Errol</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Estermont</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Farman</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Farring</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Farwynd</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Farwynd of the Lonely Light</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Fell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Flint</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Flint of Widow&#x27;s Watch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Florent</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Fossoway</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Fossoway of Cider Hall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Fossoway of New Barrel</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Frey</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Frey of Riverrun</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Gargalen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Gaunt</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Glover</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Goodbrook</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Goodbrother</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Goodbrother of Shatterstone</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Graceford</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Grafton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Greenfield</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Greenhill</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Grell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Greyjoy</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Grimm</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Haigh</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Harclay</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Hardy</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Hardyng</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Harlaw</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Harlaw of Grey Garden</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Harlaw of Harlaw Hall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Harlaw of Harridan Hill</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Harlaw of the Tower of Glimmering</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Hasty</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Hawick</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Hayford</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Heddle</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Hetherspoon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Hewett</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Hightower</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Hogg</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Hollard</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Hornwood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Horpe</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Humble</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Hunt</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Hunter</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Inchfield</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Ironmaker</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Jast</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Jordayne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Karstark</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Kenning of Harlaw</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Kenning of Kayce</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Kettleblack</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Lannister</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Lannister of Casterly Rock</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Lannister of Lannisport</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Leek</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Lefford</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Liddle</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Locke</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Longthorpe</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Lorch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Lothston</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Lychester</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Lydden</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Lynderly</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Mallery</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Mallister</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Manderly</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Manwoody</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Marbrand</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Martell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Meadows</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Merlyn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Merryweather</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Mertyns</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Mollen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Moore</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Mooton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Moreland</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Mormont</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Morrigen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Mudd</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Mullendore</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Myre</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Nayland</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Norcross</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Norrey</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Norridge</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Nymeros Martell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Oakheart</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Osgrey</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Paege</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Payne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Peake</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Peckledon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Pemford</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Penny</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Penrose</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Piper</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Plumm</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Poole</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Potter</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Prester</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Qorgyle</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Rambton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Redfort</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Redwyne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Reed</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Reyne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Rhysling</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Risley</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Roote</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Rosby</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Rowan</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Royce</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Royce of the Gates of the Moon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Ruttiger</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Ryger</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Rykker</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Ryswell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Santagar</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Sarsfield</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Seaworth</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Selmy</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Sharp</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Shepherd</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Slynt</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Smallwood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Sparr</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Spicer</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Stackspear</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Staedmon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Stark</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Staunton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Stokeworth</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Stonetree</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Stout</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Strickland</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Strong</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Suggs</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Sunglass</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Swann</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Swyft</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Tallhart</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Targaryen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Tarly</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Tarth</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Tawney</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Templeton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Thorne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Toland</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Tollett</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Toyne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Tully</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Turnberry</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Tyrell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Uffering</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Uller</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Umber</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Vaith</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Vance</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Vance of Atranta</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Vance of Wayfarer&#x27;s Rest</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Varner</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Velaryon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Vikary</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Volmark</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Vypren</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Vyrwel</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Wagstaff</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Wayn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Waynwood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Weaver</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Webber</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Wells</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Westerling</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Whent</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Willum</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Woolfield</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Wull</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Wylde</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Wythers</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Yew</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House Yronwood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House of Galare</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House of Ghazeen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House of Kandaq</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House of Loraq</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House of Pahl</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_House of Reznak</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Iron Bank of Braavos</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Khal</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Kingdom of the Three Daughters</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Kingsguard</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Kingswood Brotherhood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Maesters</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Mance Rayder</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Moon Brothers</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Night&#x27;s Watch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Peach</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Pureborn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Queensguard</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_R&#x27;hllor</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Sea watch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Second Sons</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Stone Crows</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Stormcrows</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Summer Islands</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_The Citadel</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Thirteen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Three-eyed crow</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Undying Ones</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Unsullied</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Windblown</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_Wise Masters</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>x2_wildling</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </div>\n",
       "          </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div><div class='total_features'>\n",
       "        <div class=\"features fitted\">\n",
       "          <details>\n",
       "            <summary>\n",
       "              <div class=\"arrow\"></div>\n",
       "              <div>575 features</div>\n",
       "              <div class=\"image-container\" title=\"Copy all output features\">\n",
       "                <i class=\"copy-paste-icon\"\n",
       "                  onclick=\"\n",
       "                  event.stopPropagation();\n",
       "                  event.preventDefault();\n",
       "                  copyFeatureNamesToClipboard(this);\n",
       "                  \"\n",
       "                >\n",
       "                </i>\n",
       "              </div>\n",
       "            </summary>\n",
       "            <div class=\"features-container\">\n",
       "                <table class=\"features-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr>\n",
       "          <td>num__male</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>num__isMarried</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>num__isNoble</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>num__age</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>num__numDeadRelations</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>num__popularity</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Acorn Hall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Andals</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Arbor</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Archmaester</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Ashford</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Barrowton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Bear Island</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Big BucketThe Wull</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Bitterbridge</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Blackcrown</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Blackmont</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Bloodrider</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Blue Grace</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Brightwater</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Broad Arch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Brother</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Captain</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Captain of the guard</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Captain-General</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Castellan</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_CastellanCommander</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Casterly Rock</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Castle Lychester</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Cerwyn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Cobblecat</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Coldmoat</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Coldwater Burn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Commander of the City Watch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Commander of the Second Sons</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Crag</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Crakehall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Cupbearer</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Darry</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Deepwood Motte</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Dreadfort</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Duskendale</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Dyre Den</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Eastwatch-by-the-Sea</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Eyrie</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Fair Isle</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Feastfires</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Felwood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_First Ranger</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_First Sword of Braavos</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Foamdrinker</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Ghost Hill</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Godswife</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Golden Tooth</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Goldengrove</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Goldgrass</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Good Master</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Goodman</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Goodwife</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Grand Maester</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Grassy Vale</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Greenshield</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Greenstone</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Grey Glen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Greywater Watch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Gulltown</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Hand of the King</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Harlaw</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Harrenhal</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Harridan Hill</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Hayford</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Haystack Hall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Heart&#x27;s Home</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_High Septon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_High Steward of Highgarden</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Highgarden</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Hightower</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Horn Hill</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Hornvale</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Hornwood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Ironoaks</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Karhold</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Kayce</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Keeper of the Gates of the Moon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Khal</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_KhalKo (formerly)</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Khalakka</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_King</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_King in the North</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_King of Winter</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_King of the Andals</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_King-Beyond-the-Wall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Knight</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Knight of Griffin&#x27;s Roost</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lady</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lady of Bear Island</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lady of Darry</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lady of Torrhen&#x27;s Square</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lady of the Vale</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_LadyQueen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_LadyQueenDowager Queen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Last Hearth</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Light of the West</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lonely Light</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Longsister</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Longtable</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord Captain of the Iron Fleet</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord Commander of the Night&#x27;s Watch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord Paramount of the Mander</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord Paramount of the Trident</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord Reaper of Pyke</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord Seneschal</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord Steward</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of Blackhaven</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of Coldmoat</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of Crows Nest</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of Dragonstone</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of Flint&#x27;s Finger</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of Greyshield</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of Griffin&#x27;s Roost</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of Hammerhorn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of Harrenhal</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of Oakenshield</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of Oldcastle</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of Pebbleton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of Starfall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of Sunflower Hall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of the Crossing</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of the Deep Den</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of the Hornwood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of the Iron Islands</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of the Marches</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of the Red Dunes</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of the Seven Kingdoms</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of the Snakewood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of the Ten TowersLord Harlaw of HarlawHarlaw of Harlaw</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Lord of the Tides</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Maester</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Magister</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Maidenpool</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Master of Coin</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Master of Deepwood Motte</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Master of Harlaw Hall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Master of coin</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Master of whisperers</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Master-at-Arms</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Mistress of whisperers</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Nightsong</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Oarmaster</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Old Oak</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Old Wyk</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Prince</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Prince of Dorne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Prince of Dragonstone</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Prince of Winterfell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Prince of WinterfellHeir to Winterfell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Prince of the Narrow Sea</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Princess</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_PrincessQueen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_PrincessQueenDowager Queen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Protector of the Realm</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Queen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_QueenDowager Queen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Red Flower Vale</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Redfort</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Rills</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Riverrun</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Rook&#x27;s Rest</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Ruddy Hall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Runestone</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Salt Shore</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Sandship</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Sealord</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Sealskin Point</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Seneschal</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Septa</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Septon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Ser</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_SerCastellan of Casterly Rock</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Seven Kingdoms</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Sharp Point</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Shatterstone</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Slave of R&#x27;hllor</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Starpike</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Steward</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Stokeworth</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Stonehelm</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Storm&#x27;s End</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Sunspear</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Ten Towers</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_The LiddleLord Liddle</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_The NorreyLord Norrey</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Three Towers</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Tower of Glimmering</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Tradesman-Captain</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Twins</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Uplands</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Volmark</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Warlock</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Whitewalls</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Widow&#x27;s Watch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Winterfell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Wisdom</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_Wraith</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_[1]</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_green lands</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_master of ships</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_red hand</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_the Crossing</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__title_the Dreadfort</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Andal</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Andals</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Asshai</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Asshai&#x27;i</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Astapor</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Astapori</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Braavosi</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Crannogmen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Dorne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Dornish</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Dornishmen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Dothraki</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_First Men</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Free Folk</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Free folk</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Ghiscari</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Ghiscaricari</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Ironborn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Ironmen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Lhazareen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Lhazarene</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Lysene</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Lyseni</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Meereen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Meereenese</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Myrish</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Northern mountain clans</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Northmen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Norvos</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Norvoshi</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Pentoshi</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Qarth</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Qartheen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Qohor</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Reach</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Reachmen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Rhoynar</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Rivermen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Sistermen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Stormlander</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Stormlands</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Summer Islander</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Summer Islands</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Summer Isles</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Tyroshi</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Vale</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Vale mountain clans</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Valemen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Valyrian</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Westerlands</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Westerman</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Westermen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Westeros</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Wildling</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_Wildlings</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_free folk</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_northmen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__culture_westermen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Alchemists&#x27; Guild</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Antler Men</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Band of Nine</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Black Ears</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Blacks</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Brave Companions</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Brotherhood Without Banners</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Brotherhood without Banners</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Brotherhood without banners</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Burned Men</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Chataya&#x27;s brothel</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Citadel</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Company of the Cat</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Drowned men</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Faceless Men</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Faith of the Seven</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Golden Company</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Good Masters</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Happy Port</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Ambrose</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Arryn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Ashford</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Baelish</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Ball</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Bar Emmon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Baratheon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Baratheon of Dragonstone</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Baratheon of King&#x27;s Landing</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Beesbury</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Belmore</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Bettley</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Blackberry</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Blackfyre</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Blackmont</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Blackwood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Boggs</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Bolling</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Bolton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Bolton of the Dreadfort</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Botley</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Bracken</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Brax</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Broom</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Brune of Brownhollow</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Brune of the Dyre Den</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Buckler</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Bulwer</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Bushy</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Butterwell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Byrch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Bywater</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Cafferen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Caron</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Cassel</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Caswell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Cerwyn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Charlton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Chelsted</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Chester</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Clegane</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Clifton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Cockshaw</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Codd</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Coldwater</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Condon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Connington</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Corbray</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Costayne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Crabb</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Crakehall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Crane</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Cupps</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Cuy</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Dalt</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Darklyn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Darry</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Dayne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Dayne of High Hermitage</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Deddings</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Dondarrion</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Drumm</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Dustin</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Erenford</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Errol</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Estermont</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Farman</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Farring</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Farwynd</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Farwynd of the Lonely Light</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Fell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Flint</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Flint of Widow&#x27;s Watch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Florent</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Fossoway</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Fossoway of Cider Hall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Fossoway of New Barrel</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Frey</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Frey of Riverrun</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Gargalen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Gaunt</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Glover</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Goodbrook</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Goodbrother</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Goodbrother of Shatterstone</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Graceford</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Grafton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Greenfield</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Greenhill</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Grell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Greyjoy</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Grimm</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Haigh</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Harclay</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Hardy</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Hardyng</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Harlaw</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Harlaw of Grey Garden</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Harlaw of Harlaw Hall</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Harlaw of Harridan Hill</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Harlaw of the Tower of Glimmering</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Hasty</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Hawick</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Hayford</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Heddle</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Hetherspoon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Hewett</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Hightower</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Hogg</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Hollard</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Hornwood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Horpe</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Humble</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Hunt</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Hunter</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Inchfield</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Ironmaker</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Jast</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Jordayne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Karstark</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Kenning of Harlaw</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Kenning of Kayce</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Kettleblack</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Lannister</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Lannister of Casterly Rock</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Lannister of Lannisport</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Leek</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Lefford</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Liddle</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Locke</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Longthorpe</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Lorch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Lothston</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Lychester</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Lydden</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Lynderly</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Mallery</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Mallister</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Manderly</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Manwoody</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Marbrand</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Martell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Meadows</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Merlyn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Merryweather</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Mertyns</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Mollen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Moore</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Mooton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Moreland</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Mormont</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Morrigen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Mudd</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Mullendore</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Myre</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Nayland</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Norcross</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Norrey</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Norridge</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Nymeros Martell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Oakheart</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Osgrey</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Paege</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Payne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Peake</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Peckledon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Pemford</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Penny</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Penrose</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Piper</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Plumm</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Poole</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Potter</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Prester</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Qorgyle</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Rambton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Redfort</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Redwyne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Reed</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Reyne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Rhysling</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Risley</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Roote</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Rosby</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Rowan</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Royce</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Royce of the Gates of the Moon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Ruttiger</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Ryger</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Rykker</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Ryswell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Santagar</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Sarsfield</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Seaworth</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Selmy</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Sharp</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Shepherd</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Slynt</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Smallwood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Sparr</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Spicer</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Stackspear</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Staedmon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Stark</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Staunton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Stokeworth</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Stonetree</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Stout</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Strickland</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Strong</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Suggs</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Sunglass</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Swann</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Swyft</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Tallhart</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Targaryen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Tarly</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Tarth</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Tawney</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Templeton</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Thorne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Toland</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Tollett</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Toyne</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Tully</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Turnberry</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Tyrell</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Uffering</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Uller</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Umber</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Vaith</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Vance</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Vance of Atranta</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Vance of Wayfarer&#x27;s Rest</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Varner</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Velaryon</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Vikary</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Volmark</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Vypren</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Vyrwel</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Wagstaff</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Wayn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Waynwood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Weaver</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Webber</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Wells</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Westerling</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Whent</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Willum</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Woolfield</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Wull</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Wylde</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Wythers</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Yew</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House Yronwood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House of Galare</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House of Ghazeen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House of Kandaq</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House of Loraq</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House of Pahl</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_House of Reznak</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Iron Bank of Braavos</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Khal</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Kingdom of the Three Daughters</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Kingsguard</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Kingswood Brotherhood</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Maesters</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Mance Rayder</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Moon Brothers</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Night&#x27;s Watch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Peach</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Pureborn</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Queensguard</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_R&#x27;hllor</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Sea watch</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Second Sons</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Stone Crows</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Stormcrows</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Summer Islands</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_The Citadel</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Thirteen</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Three-eyed crow</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Undying Ones</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Unsullied</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Windblown</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_Wise Masters</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>cat__house_wildling</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </div>\n",
       "          </details>\n",
       "        </div>\n",
       "    </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"model__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
       "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight=&#x27;balanced&#x27;*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;balanced&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
       "            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-penalty;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
       "            penalty\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-penalty;\">\n",
       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing &#x27;multinomial&#x27; + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-C;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-C;\">\n",
       "            C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-l1_ratio;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
       "            l1_ratio\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-l1_ratio;\">\n",
       "            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-dual;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-dual;\">\n",
       "            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; n_features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
       "            fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
       "            intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a &quot;synthetic&quot; feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-solver;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
       "            solver\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-solver;\">\n",
       "            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-warm_start;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-warm_start;\">\n",
       "            warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
       "           <td>[0,1]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-coef_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29\">\n",
       "            coef_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-coef_;\">\n",
       "            coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1, 575)</td>\n",
       "           <td>[[ 0.37,-0.04, 0.06,...,-0.26,-0.11, 0.73]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29\">\n",
       "            intercept_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
       "            intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
       "           <td>[-0.81]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>575</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_iter_,-ndarray%20of%20shape%20%281%2C%20%29\">\n",
       "            n_iter_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
       "            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int32](1,)</td>\n",
       "           <td>[58]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
       " SPDX-License-Identifier: BSD-3-Clause\n",
       "*/\n",
       "\n",
       "function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "\n",
       "    const parent = element.parentElement;\n",
       "    if (!parent || !parent.nextElementSibling) {\n",
       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "/**\n",
       " * Copy the list of feature names formatted as a Python list.\n",
       " *\n",
       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
       " *   contain a `details` element and a table containing feature named.\n",
       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
       " */\n",
       "function copyFeatureNamesToClipboard(element) {\n",
       "    var detailsElem = element.closest('.features').querySelector('details');\n",
       "    var wasOpen = detailsElem.open;\n",
       "    detailsElem.open = true;\n",
       "    var content = element.closest('.features').querySelector('tbody')\n",
       "                  .innerText.trim();\n",
       "    if (!wasOpen) detailsElem.open = false;\n",
       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
       "    const originalHTML = element.innerHTML.replace('вњ”', '');\n",
       "    const originalStyle = element.style;\n",
       "    const copyMark = document.createElement('span');\n",
       "    copyMark.innerHTML = 'вњ”';\n",
       "    copyMark.style.color = 'blue';\n",
       "    copyMark.style.fontSize = '1em';\n",
       "\n",
       "    navigator.clipboard.writeText(formattedText)\n",
       "        .then(() => {\n",
       "            element.style.display = 'none';\n",
       "            element.parentElement.appendChild(copyMark);\n",
       "\n",
       "            setTimeout(() => {\n",
       "                copyMark.remove();\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 1000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'orange';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 1000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-1');</script></body>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['male', 'isMarried', 'isNoble', 'age', 'numDeadRelations',\n",
       "       'popularity'],\n",
       "      dtype='str')),\n",
       "                                                 ('cat',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
       "                                                                  ('onehot',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                                  Index(['title', 'culture', 'house'], dtype='str'))])),\n",
       "                ('model',\n",
       "                 LogisticRegression(class_weight='balanced', max_iter=1000))])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f939feca",
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg_pred = logreg.predict(X_test)\n",
    "\n",
    "logreg_prob = logreg.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "04bf6c8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "\n",
    "plt.hist(\n",
    "    logreg_prob[y_test.values == 0],\n",
    "    bins=20,\n",
    "    alpha=0.6,\n",
    "    label=\"Живы\"\n",
    ")\n",
    "\n",
    "plt.hist(\n",
    "    logreg_prob[y_test.values == 1],\n",
    "    bins=20,\n",
    "    alpha=0.6,\n",
    "    label=\"Мертвы\"\n",
    ")\n",
    "\n",
    "plt.xlabel(\"Предсказанная вероятность смерти\")\n",
    "plt.ylabel(\"Количество персонажей\")\n",
    "plt.title(\"Распределение предсказанных вероятностей\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ad2fe0a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAHHCAYAAAAf2DoOAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAATzBJREFUeJzt3QucTPX7wPFn3Na6rPta93tKrqkkxepC8ROl+lUUERGSkFQiCqFISf270IV0cStJuUVC5Zr7Pda9EpvVLrtz/q/n6zfTzN7Mmtk5Zufz7nXamXPOnDkzc8w85/k+3+9xWJZlCQAAQJDkCtYTAQAAEHwAAICgI/MBAACCiuADAAAEFcEHAAAIKoIPAAAQVAQfAAAgqAg+AABAUOUJ7tMB8NfZs2flxIkT4nQ6pWzZsryhAEIOmQ8gBKxZs0YeeOABKVmypEREREiZMmWkffv2Ei4qV64snTt3Dtj2HA6HDBs2LGDbC3e//fabeU+nTp1q964gRBB85BD6j17/8eu0YsWKNMt1FP0KFSqY5f/5z39s2UdcnLlz58oNN9wgW7dulZdeekkWLlxoprfffjvob6n+YOsx9Mcff8ilbv78+UEJMFz/7lxTVFSUNGvWTL7++utsf24gVNHsksPkz59fpk+fbn6sPC1btkwOHjxozpoROrR55ZFHHpGWLVvK559/Lvny5ZNwtGPHDsmVK1eWg49JkyalG4D8888/kidP4L7+br31VnnooYdMkL9//36ZPHmytGnTRr755hvz2eV0lSpVMu9p3rx57d4VhAgyHzlMq1atzI9UcnKy13wNSBo2bCgxMTG27RuybsqUKZKYmGgyW+EaeCgNmgP5w6ZBeiCDj8suu0w6duwoDz74oDz33HOyaNEiE4i89tprEmwJCQlBf07N+Oh7mjt37qA/N0ITwUcOc//998uff/5p0vKeBYpffPGFqRlIz7hx4+T666+XEiVKSGRkpAlSdP3MUsupp9jYWLPe999/b+5/+umn8swzz5hgp2DBgnLHHXdIXFyc1zb1Ma7Hufzyyy/ubaZ+/t69e6fZd21C0noAT7/++qupD6hatar5QtR96NKli3lffHH8+HHp2rWrlC5d2jy+Xr168sEHH6Tbxq3vnafatWuneU36Y6Trnj592uv1pD4jHzt2rNd7qVavXi3169eXkSNHmmYz/RGuUaOGjB492hScetKAc8SIEVKtWjWznr4v+hkkJSVdsH6ie/fu5rXq5xcIS5YskRtvvNF89kWLFpW2bdvKtm3b0qynz3f11Veb59b91qYkV9NOZvt87tw5eeGFF8x7oY/VY1ezfa7jXtfVrIfyPE4ze/8PHTpkPnct4tX3r0qVKtKzZ0/z7yerrrjiClOfs2fPHq/5+lkMHTpUqlevbp5DP9OnnnoqzWekWYTHH3/cbKNw4cLm34/uX+r9dr1X2iSn/76LFSvmlfX8+OOPzb9n/XddvHhxue+++9L8O9y1a5epH9J/J/peli9f3qx36tQp9zr6vup29bMsVKiQ1KxZ0xxbF6r58OU4cL2G3bt3m89N1ytSpIg8/PDDcubMmSy/9wgNNLvkMPol3bhxY/nkk0/k9ttvN/M09atfJPqFMnHixDSP0bMz/XLr0KGD+aKdMWOG3HPPPTJv3jxp3bq1Weejjz5yr//DDz/I//3f/8n48ePNl6PSH2pPWpugXyiDBg0yP+YTJkyQW265RTZs2GC+CDOi6/tLvyj37t1rvrz0C3XLli1mf/Wv/pin/mFL/aWvP/76RajBjv4AaSZJvxRPnjwpffv2leyg2x41alSa+RowaQ2PThpA6Q/J4sWLZfDgweYL/6233nKvq80zGiTdfffd0r9/f/npp5/MNvXLfvbs2Rk+t/4YvvfeeyZgTB04XQw969djT4M//WHR9/T111+XJk2ayLp169zB4vr16+W2224zxbMaSKSkpMjw4cOlVKlSF3wO3a6+Nn3N1157rcTHx5uiXN2+NoE8+uijcvjwYXMseB67GdF1dTv6OWggdvnll5sfew3C9Qcwq1kn/ff2119/mYDKRYNF/Xemn6U+hwYomzZtMv+Odu7cKXPmzHGvq8fbZ599ZjIp1113nWk2df1bTI/+e9VATINUzbi4/g0OGTJE7r33XvM+/f777+ZzaNq0qXnv9Ude/71rs5AGP3369DH/XvR16799fS80CNB/Nxrk161b13w+GjTpv48ff/wxIMeBi+6n/nvTz1WXv/vuuxIdHS0vv/xylt57hAgLOcKUKVP0G8f65ZdfrDfeeMMqXLiwdebMGbPsnnvusZo3b25uV6pUyWrdurXXY13ruZw9e9aqXbu2ddNNN2X6XPv27UuzbOnSpWZZuXLlrPj4ePf8zz77zMx/7bXX3POaNWtmJpf58+ebdW677Tbz15Pe79WrV5rn09eirymz16M++eQTs43ly5dbmZkwYYJZ7+OPP/Z6Pxo3bmwVKlTI/Zr0tet6Y8eO9Xr8lVde6fWa1LPPPmvW/fvvv71ez9ChQ933n3rqKSs6Otpq2LCh1+P1tq47bNgwr2127tzZzN+0aZO5v2HDBnP/kUce8VpvwIABZv6SJUvc8/T96tSpk7n99ttvm+Wvv/665QvdZ13/999/z3Cd+vXrm9fy559/uudt3LjRypUrl/XQQw+557Vp08YqUKCAdejQIfe8Xbt2WXny5Enz+Xvus6pXr16a4zg1PV4y+opL/f7rfun+6b+f1JxOZ6bPo9vq2rWreU+OHz9urVmzxn0Mex4fH330kXmOH374wevxb731lln3xx9/NPfXrl1r7j/xxBPpfuae++36PO6//36vdX/77Tcrd+7c1ksvveQ1X48XfX9d89evX28e//nnn2f4+saPH3/Bz9z170G/G7J6HLheQ5cuXby2eeedd1olSpTI8DkR2mh2yYH0DELPMvTs5e+//zZ/M2pyUZ6ZCD1b07M2TZXq2cfF0uI7TRe76Nm4nuFqEWB69Dtcz+Y1/duoUSPxh+fr0XoJ7ZmhZ4/qQq9J90/P/rT5ykVrDTQFrs0megYaaHqmqWeEepaqKe3UtB29X79+XvM0s6FcPSpc7+uTTz6Z6Xqpe9E89thjMnDgwHSbtC7GkSNHTHZLz9w1ze+iZ82akXDtp2Y59My4Xbt2XmOVaHOEK2OXGT1r1zNybTLwl2YkNOugBaLaBJRaZpkyF80cacZGz9R1G5qd0uYUz89DM2ia7dCsih6Trummm24yy5cuXWr+LliwwPzVz8aTZiYy0qNHD6/7s2bNMq9Lvws8n0uPbc2QuJ5LMxvq22+/zbCJQ99r1/GSuqnP3+Mgs9eg30Ga+dOsFnIego8cSL8EtYlDi0z1S0i/6PXHPyManOiPs7b36heFPl6r9T3bfLNKv+BSf4HrD4s2FaRn2rRp5sdE08aB6CGizSPaFKSBiL4eTeeqC70m7amg+566Z4X+aLiWB5o2e+gPsDYVpKbvmy7T7puetM1d99H1fup+6X19jz3pj43+eKTeb/1h0ABLjw19vwLF9Ty6f6npe6g/gFoQqU1xGiCn3l+V3rzUNP2vzQJa6FmnTh0TQGmtz8XQ5gj9gdN6nYultQzaxKNBnquGQX/MPY8jDZT0GNfj0XPS16D0PfH8LF3HrC/vS+p19bk0oNdjOfXzaTOc67n0cRogaROHNqFqE4zWynj+O/nvf/9rmkq06Ub/TWnzrTYJZRaI+HoceKpYsaLXfa1fcZ0QIeeh5iOH0kxHt27d5OjRo+ZM0nX2kprWb2g7tLYDv/nmmyY7oWf62stCg5dg0HZnPevXYj/XF7E/9Gxv5cqV5gdJizU1m6BflFpf4OuZW7DoD4EW6WlhYHq9OTKrj0mPL2fpauPGjea4uPnmm837pD01AlHvESx6vGoxp56Nf/fdd+bHU2sntAZGfySDTYs0NeB39TjTH3LNJjVv3lzuuusuM1+PPQ2UXn311XS3ocWnFyv1caLPpceC1nul1wPFM8P2yiuvmAyF673ULJ/WXWh9lL4u3fby5ctNtkSDK83MaH2QZmx0/UD1cMloO64aFuQsBB851J133mnOpPULRL8oMjJz5kyT8dC0q+cYIBp8+CN1Oly/QLRITdOuqWnQo2digRgQSs+SNOWtBYzPP/98hvuT2XgFegatX96eZ63bt293Lw8kbWrSAEnPLtOjZ6b6Ba/NZ57NWFqgqPvoKtrT/dL7+jpdWRp17NgxkyFIvd/6I6jNAPrDon+1AFJftx4L/nA9j47LkZq+h/qjrD0f9Hl00mMitfTmpUezdFpUrJM2iWlAoseQK/jwNRDTbIBmljZv3iyBov/2NBjSnk76b1H3RYtPNejTgC+zfXN9lvv27fPKIPr6vih9Lv03p8ePLwG9Hg866f5q4K6ZDg3kXnzxRbNc/y3ofuukwZNmKJ999lkTkLiCrtSvwZfjAOGLZpccSs9stOlEv4y1LTuzsw39ItT0u4um8j0r7y/Ghx9+aH4wXbTXgLYDp27P13W0Kl9rGgIxBonr7Cn12ZL2tvGFnrVqtsgzYNMurFqToe+pjlwZKKtWrTJnm9ptNqMfI90f/WzeeOMNr/mus2dXDwhdL73XmXo9l6uuusp8+euPimYN9DPXpgx/aeZMgyntdaNBj4v+sGsQ5dpP/Zz0R0uPM+1p4vkDq2frF5K627R+Ntos4dll1fXj5rkf6dH3QGtPvvrqK9NjJhBn3jqGiNbbaGZLP2NXRk7re955550062sTlKsZwjUomQblnvQY9JVmW/Q91iA89f7rfdf7p81NqccE0iBE3xPXe5les5x+xip1F+GsHgcIX2Q+crBOnTpdcB39UdIfKG2S0KYazUBom69+kV9sG7rrrFTHBdCzUj371h9F3aY2BXnSAlA9C9LivAs5cOCAuxjPs71ev7h1vgYGegarZ8BjxowxY0GUK1fOfNnpWaQvNAOgY01oGnrt2rUms6CBk3Yr1NfgmX1wndl57pOegesXt+c87fabHt0vLb5L78zRRb+kdbmeZepr0C90HTtBM1ZaoOeqU9CxSPTz1i7F+mWv78XPP/9svvz1h1XT/xnRbWgXZw2CtD0/vexUanrMFChQwGuevm4d+0HHK9EgU7t8a1Oaq4ulFjemHqNC3wM9y9bxNFxBlu6P1qRkplatWqaZSLse67GmQYN+Tp6Fs7pMaTOC/qDrj7G+vvTombzui75vrm6wGixrVki7xmbUbJkZPYY0+6ZdRfUz0G6zWiuhn5tmDPR162vWTIDO1+yjFqvqfmvhtR5vGiS4utpqtsvXjI5mPjRr4eqSrc+vx64eQ9rtWl/jgAEDzLGk75l21dUMiQYi2jVZ3yvXtYM0KNVmF/2u0IyGfkdoYKRNMqlHUvbk63GAMGV3dxsEvqttZtLravvee+9ZNWrUsCIiIqzLL7/cbMvV/e1iu9pq19bBgwebrnaRkZHmOffv3++1rqsbqXbl85Tec+v9C02u/Tl48KDpple0aFGrSJEipqvx4cOH03RTzMixY8eshx9+2CpZsqSVL18+q06dOl5dCD27FmZlSt3V1uFwmG6Vqd+T1F11T58+bfXr188qW7aslTdvXqt69erW6NGjrZSUFK/1zp07Z73wwgtWlSpVzHoVKlQwn0FiYmKm3VaVrqOf/TXXXGMlJydn+N64Ppv0Ju3a6bJo0SKrSZMm5rOPiooy3Wq3bt2aZnuLFy+2GjRoYN7natWqWe+++67Vv39/K3/+/Jnu84svvmhde+215jPW59B91+6j2i3aRV9Hnz59rFKlSpn32vOYSu9Y0ONTu4Dq+vpvoWrVqqa7blJSUobvR2bdwJV2kdbl+u9C6f69/PLLpku2PkexYsVM92r93E6dOuV+XEJCgtlm8eLFTRfvdu3aWTt27DDb0s8+9eeRUTfYmTNnWjfccINVsGBBM+n7pNvVbam9e/eaLq763ut7rs+n3fL18/P8jNq2bWuOP/2c9K927d25c2emXW19PQ4yeg2Zfc8g9Dn0f3YHQMg5dMRKPcvWM8bMetgEkp7Zadu2ntWlHrgIoUfP0gPVjTYn0WxQgwYNTHGyDggIhDJqPgDYRlPxnjTg0DEgQqnnTTDeF6XNMNq0pc2KQKij5gMhT3tsaJt+Vrulwn469LbrOjyuq8HqUOa+1ADlZFqzpDVHmkXU4lUtwtVJazX86ZILXCoIPhDydOCj1IWoCA1a6KzXIdIeRtrVW4sTtfgz9SB14UYv9KiDlumFArWIWQfg0iJNLTwGcgJqPgAAQFBR8wEAAIKK4AMAAAQVNR8BpsMi64iNOqCPr8M7AwAuDTr6hI68rBd0TH2ByUBKTEw017UKBC3S9vfSCMFG8BFgGnhQjQ4AoS0uLs6M4ppdgUeVSoXk6PF/L2vhD700hY5zFEoBCMFHgLmG396/rrJEFaJVCznT7Y93tnsXgGyRnJwoaxaNTHMphUA6e/asCTz2r60sUYX9+52I/9splRr+ZrZJ8BHGXE0tGnj4e1ABl6o8eUPnDAu4GMFoNi9U2GEmfzglNJv3yXwAAGCDFMspKZb/2whFBB8AANjAKZaZ/N1GKKJdAAAABBWZDwAAbOA0//m/jVBE8AEAgA1SLMtM/m4jFNHsAgAAgorMBwAANnCGccEpwQcAADZwiiUpYRp80OwCAACCiswHAAA2cNLsAgAAgimF3i4AAADBQbMLAAA2cP5v8ncboYiCUwAAbJDyv94u/k6+GjVqlFxzzTVSuHBhiY6Olnbt2smOHTu81omNjTVX9PWcevTo4bXOgQMHpHXr1lKgQAGznYEDB0pycnKWXjuZDwAAbJBinZ/83Yavli1bJr169TIBiAYLzzzzjLRo0UK2bt0qBQsWdK/XrVs3GT58uPu+Bhnu50tJMYFHTEyMrFy5Uo4cOSIPPfSQ5M2bV0aOHOnzvhB8AAAQBhYsWOB1f+rUqSZzsXbtWmnatKlXsKHBRXq+++47E6wsWrRISpcuLfXr15cRI0bIoEGDZNiwYZIvXz6f9oVmFwAAbKz5cPo5qfj4eK8pKSnpgs9/6tQp87d48eJe86dNmyYlS5aU2rVry+DBg+XMmTPuZatWrZI6deqYwMOlZcuW5jm3bNni82sn8wEAgA2c4pAUcfi9DVWhQgWv+UOHDjWZiAwf53TKE088IU2aNDFBhssDDzwglSpVkrJly8qvv/5qMhpaFzJr1iyz/OjRo16Bh3Ld12W+IvgAACDExcXFSVRUlPt+REREputr7cfmzZtlxYoVXvO7d+/uvq0ZjjJlysjNN98se/bskWrVqgVsf2l2AQDABk4rMJPSwMNzyiz46N27t8ybN0+WLl0q5cuXz3QfGzVqZP7u3r3b/NVakGPHjnmt47qfUZ1Iegg+AACwQcr/ml38nXxlWZYJPGbPni1LliyRKlWqXPAxGzZsMH81A6IaN24smzZtkuPHj7vXWbhwoQl4atWq5fO+0OwCAEAY6NWrl0yfPl3mzp1rxvpw1WgUKVJEIiMjTdOKLm/VqpWUKFHC1Hz069fP9ISpW7euWVe75mqQ8eCDD8qYMWPMNp577jmz7Qs19Xgi+AAAwAYpASg4zcrjJ0+e7B5IzNOUKVOkc+fOppusdqGdMGGCJCQkmCLW9u3bm+DCJXfu3KbJpmfPniYLouODdOrUyWtcEF8QfAAAYAOn5TCTv9vISrNLZjTY0IHILkR7w8yfP1/8Qc0HAAAIKjIfAACEQbPLpYTgAwAAG6RILjP5t43QRPABAIANrADUfOg2QhE1HwAAIKjIfAAAYIMUaj4AAEBQgw8rl5n824aEJJpdAABAUNHsAgCADZziEKefOQCnhGbqg+ADAAAbpIRxzQfNLgAAIKjIfAAAELIFp5aEIoIPAABsq/lw+L2NUESzCwAACCoyHwAA2MAZgGu70NsFAAD4LIWaDwAAEOzMhzNMMx/UfAAAgKCi5gMAABukWA4z+buNUETwAQCADVICUHCaQrMLAADAhZH5AADABk4rl5n820ZoFpwSfAAAYIMUml0AAACCg8wHAAA2cAagt4puIxQRfAAAELKDjOWSUBSaew0AAEIWmQ8AAEL22i65JBQRfAAAYAOnOMzk7zZCEcEHAAA2SAnjzEdo7jUAAAhZZD4AAAjZQcZySSgi+AAAwAZOy2Emf7cRikIzZAIAAFkyatQoueaaa6Rw4cISHR0t7dq1kx07driXnzhxQvr06SM1a9aUyMhIqVixojz++ONy6tQpr+04HI4004wZM7K0L2Q+AACwgTMAzS5ZGWRs2bJl0qtXLxOAJCcnyzPPPCMtWrSQrVu3SsGCBeXw4cNmGjdunNSqVUv2798vPXr0MPO++OILr21NmTJFbrvtNvf9okWLZmm/CT4AAAjZq9rm8nndBQsWeN2fOnWqyYCsXbtWmjZtKrVr15aZM2e6l1erVk1eeukl6dixowlW8uTJ4xVsxMTEXPR+0+wCAECIi4+P95qSkpIu+BhXc0rx4sUzXScqKsor8FCaQSlZsqRce+218v7774tlWVnaXzIfAADYIEUcZvJ3G6pChQpe84cOHSrDhg3L8HFOp1OeeOIJadKkicl4pOePP/6QESNGSPfu3b3mDx8+XG666SYpUKCAfPfdd/LYY4/J6dOnTX2Irwg+AAAI8WaXuLg4k6FwiYiIyPRxmrnYvHmzrFixIt3lmj1p3bq1qf1IHcQMGTLEfbtBgwaSkJAgY8eOzVLwQbMLAAAhLioqymvKLPjo3bu3zJs3T5YuXSrly5dPs/zvv/82xaTaK2b27NmSN2/eTJ+7UaNGcvDgQZ+aelzIfAAAYIMUj2YTf7bhK63L0K60GlB8//33UqVKlXQzHi1btjTBy5dffin58+e/4HY3bNggxYoVu2C2xRPBBwAAYdDbpVevXjJ9+nSZO3euyWocPXrUzC9SpIgZ10MDD+16e+bMGfn444/dxauqVKlSkjt3bvnqq6/k2LFjct1115nAZOHChTJy5EgZMGBAlvab4AMAgDC4sNzkyZPN39jY2DRjdnTu3FnWrVsnP/30k5lXvXp1r3X27dsnlStXNk0wkyZNkn79+plMiq736quvSrdu3bK03wQfAACEAesC3WE1KLnQOloL4jm42MUi+AAAwAaWOMTpZ82HbiMUEXwAABAGzS6XktDcawAAELLIfAAAYAOn5TCTv9sIRQQfAADYICUAV7X19/F2Cc29BgAAIYvMBwAANnDS7AIAAIIafEguM/m7jVAUmnsNAABCFs0uAADYIMVymMnfbYQigg8AAGzgpOYDAAAEkxWAq9rqNkJRaO41AAAIWTS7AABggxRxmMnfbYQigg8AAGzgtPwfHl23EYpodgEAAEFF5gOXnBmvR8uP84tK3O4IyZffKbWuPiNdnz0sFaonea23dU0BmfpyGdm+roDkzi1S9cp/ZOT0PRIRef5UYPprpeXnRVGyd0uk5Mlnyaztm2x6RYC3ujWOyP0tfpXLKv0hJYuekWffvFVWbKjssYYlXe5YK/+5cbsUijwrm/aUllen3SCHjhcxS2NK/C0PtV4nV11+WIpH/SN/nCogC1fXkI/m15fklNy83SHCGYCCU38fb5eQ2uudO3dK6dKlpWfPnl7zv//+e3E4HHLy5En3vMTERGnevLk0aNDAaz4ufb+uKiRtOv8hE+btklEz9khKssgz91eTxDO5vAKPZztUk4ZN/5aJ83fJxPk75Y6H/xCHxxGdfNYhTduclNad/rDnhQAZiIxIlt0Hi8uE6denu/z+lhvlrpu2yCsf3yA9RrWVxKS8Mq7vN5IvT7JZXjHmpORyiIz7+EbpNOxueeOzxnJHs23S7c5feM9DiFMcAZlCUUhlPi677DJZsGCBCSqKFi0qo0aNSne95ORkueeee+Tw4cPyww8/mHUROkZO3+t1v/+EA/LfOnVk16+RUue6BDPv7WHlpF3X3+W/fY6710udGXlo4FHz97tPiwdlvwFf/bS5gpnSZ8k9t2yWj75uID9uPJ8NGTklVmaP+1huaLBflvxSTX7eUsFMLkf+iJJPS5+Uts22yeQvruODwCUvpDIfSjMZX3/9tUycOFHGjBmTZrnT6ZROnTrJxo0bZeHChRIdHW3mV65cWSZMmOBeb/HixSZb0q5dO/e82NhYM0+nyMhIqV+/vgl2YK+E+PNp5MJFU8zfk3/kke3rCkrREsnyRJsa8t+6V8qAu6rL5p8K2ryngP/KlPxbShT5R9ZuK+eel/BPPtm2r5RcWfVYho8rGHlW4hMi+AhCcITTFD+nUBRywYdq0qSJzJo1S4YMGSJvv/2217JevXqZoGPRokVSsWLFdB+vAUr//v2lUKFCaZZ169ZNjhw5Ips3b5batWubQAb2cTpF3hpaTq685rRUvjzRzDuyP5/5+9GrMXJ7hz/lpWl7pXqdM/L0f6vJob3nlwGhSms41Im/I73m/xUf6V6WWrlSp0wzzVfLrwjKPiKwNR9OP6dQFFLNLp6KFCligog+ffpIly5dzLzBgwfLu+++K1FRUZk2tXzwwQeSlJQkbdu2ldOnT3stK1CggMTExJimG82a6PNkRrejk0t8fLzfrw3/euOZ8rJ/e6S8MmeXV0CiWnX8U1red8Lcrl7nH9mworB8O6OEdHnmCG8hwkbJogkypu8C+X5NVZm34nK7dwfwSUiGTGfPnpVHHnlEevToIW+++aY7+6FBhTanXHnlldK7d+90H3vmzBl57rnnTJNNnjxpYy/dnmZEtNnlo48+MtvMjNadaIDimipUyKgdF1n1xjPl5KeFUTLmi91Squw59/wSpc8X3VW67HwmxKVC9UQ5figvbzRC2on48xmP4oW9sxzFov5xL3MpUSRBJvSfJ1v2RJviU4QWpxaMWn5OIVpwGpLBx0svvWQyFvrDr0HI8OHDzfxPP/1UmjZtarIfX331lcydOzfNY8eOHSs1a9aUNm3apLvtDh06yIYNG2T9+vXSuXNnU7iaWTZDsy2nTp1yT3FxcQF8peHJss4HHisXFJExn++WmIpnvZaXrnBWSsSclYN7vNu3D+2NkOjy/wYpQCg68kdh+fNUpFx1xSH3vAL5z8oVVX6XLXtLe2U8XhswT3buLyWjpzYTK0Tb/sOZFYCeLrqNUBRyzS5aizF69Gj58ssv3TUbN954o9df7RUzdOhQeeyxx0wRqavpRGs5Jk+eLMuWLctw+7pu9erVzW3dxrhx4+Tnn3+WW265Jd31IyIizITANrUsnV1Mhk3ZK5GFnHLi+PnDtGDhFDOGh8MhcnfP3+WjcTFStdY/ZnyPRZ8Xl7g9+eW5d35zb+f4wbzy98k8JhviTBHZs/n8WWPZKkkSWfB/bTeADSIjzkm5UvFeRabVy/8p8Wci5PiJQvL5otryUKv1cvB4ETn6R2Hp0naN/HmygKxYX+nfwKP/PDl6opC8+UUjKVr43yzgifgCfKYhwslVbUOD1nhopuO+++6Tli1bZrrugAED5LPPPjN/33nnHTNv0qRJ0r59e9NjJiPaLHP06FFTx6FNLto04wpGEBzzPihp/g5sX8Nrfv/xB6TFf8/XeNzV7Xc5l+gwxah/n8wtVWslyqhP9kjZyv9mST4cV0YWfvZvN9vHWtQ0f7UZp9713rU+QDDVrPS7vDbga/f93veuNn+/WVlDRk+NlU++rWfGAhnQ8QcpVOCsbNpdWga+dpucTT4fiF99xSEpXzreTDPHTPfadrPu3fgwcckLqczHa6+9Jr/99pvMnz//gutq0PD+++/LtddeKw888IA7eNEmm8xooKJTvnz5pEaNGjJt2jTTTRfB8+3hDT6tp2N8eI7zkdqACQfMBFxqNuwse4EgwSHvf3m1mdKzYNVlZkJoc4bxCKcOy9IWdgSK1odo081fO6tKVOHQPCiAC2nWvTtvEnKk5HOJsnrB86aGT3tOZufvRNvvukjegv4ND3Au4azMbfF+tu5vduDXEQAABFVINbsAAJBTOANwbZZQ7WpL8AEAgA2cYdzbhWYXAAAQVGQ+AACwgZPMBwAAsCP4cPo5+UpHBb/mmmukcOHC5tplelX3HTt2eK2TmJhoLtBaokQJM5Cnjo117Jj31ZQPHDggrVu3NtdC0+0MHDjQXA8tK2h2AQAgDCxbtswEFqtXrzZXfz937py0aNFCEhIS3Ov069fPXJ7k888/N+sfPnxY7rrrLvfylJQUE3joNdZWrlxpBuOcOnWqPP/881naF5pdAAAIg2aXBQsWeN3XoEEzF2vXrjXXRdOxQt577z2ZPn263HTTTWadKVOmyBVXXGECluuuu06+++472bp1qyxatEhKly4t9evXlxEjRsigQYNk2LBhZoBOX5D5AADABpZHd9uLv7DcxdNgQxUvfv4yFBqEaDbE81pml19+uVSsWFFWrVpl7uvfOnXqmMDDRS93ogOnbdmyxefnJvMBAECIZz7iU119/UIXPdXLjTzxxBPSpEkTqV27tpmn1zXTzEXRokW91tVAQ5e51vEMPFzLXct8ReYDAIAQV6FCBTNku2vS4tLMaO2HXiV+xowZYgcyHwAAhHjmIy4uzuvaLpllPXr37i3z5s2T5cuXS/ny5d3zY2JiTCHpyZMnvbIf2ttFl7nW+fnnn7225+oN41rHF2Q+AAAI8a62UVFRXlN6wYdeR1YDj9mzZ8uSJUukSpUqXssbNmwoefPmlcWLF7vnaVdc7VrbuHFjc1//btq0SY4f//eK4tpzRp+zVq1aPr92Mh8AAISBXr16mZ4sc+fONWN9uGo0tJkmMjLS/O3atas8+eSTpghVA4o+ffqYgEN7uijtmqtBxoMPPihjxowx23juuefMtjPLtqRG8AEAQBh0tZ08ebL5Gxsb6zVfu9N27tzZ3B4/frzkypXLDC6WlJRkerK8+eab7nVz585tmmx69uxpgpKCBQtKp06dZPjw4Vnab4IPAABsYFkOM/m7Dd/XvXDH3Pz588ukSZPMlJFKlSrJ/PnzxR/UfAAAgKAi8wEAgA2c/xsozN9thCKCDwAAbODkqrYAAADBQeYDAIAwKDi9lBB8AABgA2cYN7sQfAAAYAMrjDMfdLUFAABBReYDAAAbWAFodgnVzAfBBwAANrBM8OD/NkIRzS4AACCoyHwAAGADpzjMf/5uIxQRfAAAYAOL3i4AAADBQeYDAAAbOC2HOBhkDAAABItlBaC3S4h2d6G3CwAACCqaXQAAsIEVxgWnBB8AANjAIvgAAADB5AzjglNqPgAAQFDR7AIAgA2sMO7tQvABAIBtwYfD722EIppdAABAUJH5AADABha9XQAAQFCDDzk/+buNUESzCwAACCqaXQAAsIFFswsAAAhu9CFh2+5C5gMAADtY/l/bRbcRiqj5AAAAQUXmAwAAG1iMcAoAAIIbfDgCMMIpzS4AAAAXRM0HAAB2sByBmbJg+fLl0qZNGylbtqw4HA6ZM2eO13Kdl940duxY9zqVK1dOs3z06NFZ2g9qPgAACJOaj4SEBKlXr5506dJF7rrrrjTLjxw54nX/m2++ka5du0r79u295g8fPly6devmvl+4cOEs7QfBBwAAYeL22283U0ZiYmK87s+dO1eaN28uVatW9ZqvwUbqdbOCZhcAAOwcZMzycxKR+Ph4rykpKcnv3Tt27Jh8/fXXJvORmjazlChRQho0aGCaZJKTkwOf+fjyyy993uAdd9yRpR0AACAcWQHs7VKhQgWv+UOHDpVhw4b5te0PPvjAZDhSN888/vjjctVVV0nx4sVl5cqVMnjwYNNc8+qrrwY2+GjXrp1PG9Oik5SUFJ+fHAAA+C8uLk6ioqLc9yMiIvze5vvvvy8dOnSQ/Pnze81/8skn3bfr1q0r+fLlk0cffVRGjRrl8/P6FHw4nc6s7jMAAAjStVmioqK8gg9//fDDD7Jjxw759NNPL7huo0aNTLPLb7/9JjVr1sz+gtPExMQ0EREAAAjtQcbee+89adiwoekZcyEbNmyQXLlySXR0dPYVnGqzyogRI6RcuXJSqFAh2bt3r5k/ZMgQs7MAACC4Bae+On36tAkWdFL79u0ztw8cOOBeRwtWP//8c3nkkUfSPH7VqlUyYcIE2bhxo/n9nzZtmvTr1086duwoxYoVy77g46WXXpKpU6fKmDFjTDuPS+3ateXdd9/N6uYAAECQrFmzxvRQ0clVv6G3n3/+efc6M2bMEMuy5P7770/zeK3p0OXNmjWTK6+80sQEGnz83//9X5b2I8vNLh9++KF5kptvvll69Ojhnq+pme3bt2d1cwAAhCnH/yZ/t+G72NhYE1hkpnv37mZKj/ZyWb16tfgry8HHoUOHpHr16ukWpZ47d87vHQIAICxYASg4DVDBarBludmlVq1apgo2tS+++MKdxgEAAAhY5kPbhTp16mQyIJrtmDVrlumOo80x8+bNy+rmAAAITxaZD5+1bdtWvvrqK1m0aJEULFjQBCPbtm0z82699dbs/JgAAMg5rOBf1fZScVHjfNx4442ycOHCwO8NAADI8fL4011HMx6uOhAdjAQAAPjGss5P/vD38SETfBw8eND0/f3xxx+laNGiZt7Jkyfl+uuvN31/y5cvnx37CQBAzmJR8+EzHfFMu9Rq1uPEiRNm0ttafJreaGgAAAB+ZT6WLVtmLqHrefEYvf3666+bWhAAAOADKwAFo+FScFqhQoV0BxPTa76ULVs2UPsFAECO5rDOT/5uIywGGRs7dqz06dPHFJy66O2+ffvKuHHjAr1/AADkTFbwLywXUpkPvVKdw/FvaichIUEaNWokefKcf3hycrK53aVLF2nXrl327S0AAAh5PgUfevlcAAAQQBY1H5nS4dQBAEAggw8J2wvLXfQgYyoxMVHOnj3rNS8qKsrffQIAADlYlgtOtd6jd+/eEh0dba7tovUgnhMAAPCBFb4Fp1kOPp566ilZsmSJTJ48WSIiIuTdd9+VF154wXSz1SvbAgAAH1jhG3xkudlFr16rQUZsbKw8/PDDZmCx6tWrS6VKlWTatGnSoUOH7NlTAACQI2Q586HDqVetWtVd36H31Q033CDLly8P/B4CAJCTe7tYfk4hKMvBhwYe+/btM7cvv/xy+eyzz9wZEdeF5gAAgG8jnDr8nMIi+NCmlo0bN5rbTz/9tEyaNEny588v/fr1k4EDB2bHPgIAgBwkyzUfGmS43HLLLbJ9+3ZZu3atqfuoW7duoPcPAICcyWKcj4umhaY6AQAABCzzMXHiRPHV448/7vO6AACEK0cArkrryMnBx/jx433amF58juADAAD4HXy4erfAd3deVkfyOPLyliFHKlTpiN27AGSLZGdS8N5ZiwvLAQCAYLLCt+A0y11tAQAAbLuqLQAAuEhW+GY+CD4AALCBIwAjlIbNCKcAAABBDz5++OEH6dixozRu3FgOHTpk5n300UeyYsUKv3YGAICwa3ax/JzCIfiYOXOmtGzZUiIjI2X9+vWSlHS+W9KpU6dk5MiR2bGPAADkPBbBh89efPFFeeutt+Sdd96RvHn/HceiSZMmsm7duuz5gAAAgN+WL18ubdq0kbJly5qBQefMmeO1vHPnzma+53Tbbbd5rXPixAnp0KGDREVFmavZd+3aVU6fPp29mY8dO3ZI06ZN08wvUqSInDx5MqubAwAgrAtOHX5OWZGQkCD16tUzV6TPiAYbR44ccU+ffPKJ13INPLZs2SILFy6UefPmmYCme/fu2dvbJSYmRnbv3i2VK1f2mq/1HlWrVs3q5gAACE9W8Ec4vf32282UmYiICPNbn55t27bJggUL5JdffpGrr77azHv99delVatWMm7cOJNRyZbMR7du3aRv377y008/mXTM4cOHZdq0aTJgwADp2bNnVjcHAEB4sgJX8xEfH+81ueoxL8b3338v0dHRUrNmTfO7/ueff7qXrVq1yjS1uAIPdcstt0iuXLlMXJBtmY+nn35anE6n3HzzzXLmzBnTBKNRkgYfffr0yermAACAnypUqOB1f+jQoTJs2LAsb0ebXO666y6pUqWK7NmzR5555hmTKdGgI3fu3HL06FETmHjKkyePFC9e3CzLtuBDsx3PPvusDBw40DS/aJFJrVq1pFChQlndFAAAYcsRwEHG4uLiTAGoiyYFLsZ9993nvl2nTh2pW7euVKtWzWRDNOlg+win+fLlM0EHAACwd3j1qKgor+AjULSWs2TJkibZoMGH1oIcP37ca53k5GTTAyajOpGABB/Nmzc32Y+MLFmyJKubBAAAl6CDBw+amo8yZcqY+zq4qPZsXbt2rTRs2ND9u6/lGI0aNcq+4KN+/fpe98+dOycbNmyQzZs3S6dOnbK6OQAAwpMVgGuzZPHxWiqhWQyXffv2md9wrdnQ6YUXXpD27dubLIbWfDz11FNSvXp1M7iouuKKK0xdiHY+0TG/NAbo3bu3aa7xtafLRQUf48ePT3e+FrZkdZARAADClhX8q9quWbPGtGC4PPnkk+avJg8mT54sv/76q3zwwQcmu6HBRIsWLWTEiBFeNSTaw1UDDm2G0V4uGqxMnDjRnqva6rVerr32WtPPFwAAXHpiY2PFsjKOWL799tsLbkMzJNOnT/drPwIWfGg3nPz58wdqcwAA5GxW8DMfl4osBx/a/9eTRlA6/KqmcoYMGRLIfQMAIMdyBLCrbY4PPvQaLp60vUdHQRs+fLhpGwIAAAhY8JGSkiIPP/ywGXikWLFiWXkoAABA1q/tokOranaDq9cCAHDpXNsl1GT5wnK1a9eWvXv3Zs/eAAAQZjUfDj+nsAg+XnzxRXMRuXnz5plC09RX0gMAAAhIzYcWlPbv319atWpl7t9xxx1ew6xrrxe9r3UhAADAB1Z4vks+Bx865GqPHj1k6dKl2btHAACEA4txPi78Hv1vRLRmzZpl/wcCAAByrCx1tc3sarYAACALv6kWg4z55LLLLrtgAHLixAmOPQAALsSi2cXnuo/UI5wCAABkW7PLfffdJ9HR0Vl6AgAAkJaDZpcLo94DAIAAssK32SVXVnu7AAAABKXZxel0+vVEAADAQxhnPrJU8wEAAALDQc0HAAAIKit8Mx9ZvrAcAACAP2h2AQDADlb4Zj4IPgAAsIEjjGs+aHYBAABBReYDAAA7WDS7AACAIHLQ7AIAABAcNLsAAGAHi2YXAABA8BEU9HYBAABBRbMLAAA2cPxv8ncboYjgAwAAO1jUfAAAgCBy0NUWAAAgOGh2AQDADlb4NrvQ2wUAALsDEOsipyxavny5tGnTRsqWLSsOh0PmzJnjXnbu3DkZNGiQ1KlTRwoWLGjWeeihh+Tw4cNe26hcubJ5rOc0evToLO0HwQcAAGEiISFB6tWrJ5MmTUqz7MyZM7Ju3ToZMmSI+Ttr1izZsWOH3HHHHWnWHT58uBw5csQ99enTJ0v7QbMLAABhUnB6++23myk9RYoUkYULF3rNe+ONN+Taa6+VAwcOSMWKFd3zCxcuLDExMRe302Q+AAAI0SYX69+ml/j4eK8pKSkpILt46tQp06xStGhRr/nazFKiRAlp0KCBjB07VpKTk7O0XTIfAACEuAoVKnjdHzp0qAwbNsyvbSYmJpoakPvvv1+ioqLc8x9//HG56qqrpHjx4rJy5UoZPHiwaXp59dVXfd42wQcAACHe7BIXF+cVIERERPi1XS0+vffee8WyLJk8ebLXsieffNJ9u27dupIvXz559NFHZdSoUT4/L8EHAAAh3tU2KirKK/gIROCxf/9+WbJkyQW326hRI9Ps8ttvv0nNmjV9eg6CDwAA4BV47Nq1S5YuXWrqOi5kw4YNkitXLomOjhZfEXwAABAmvV1Onz4tu3fvdt/ft2+fCR60fqNMmTJy9913m2628+bNk5SUFDl69KhZT5dr88qqVavkp59+kubNm5seL3q/X79+0rFjRylWrJjP+0HwAQBAmIxwumbNGhM4pK7f6NSpkylQ/fLLL839+vXrez1OsyCxsbGmpmPGjBlmXe1RU6VKFRN8eNaB+ILgAwCAMAk+YmNjTRFphpvLZJnSXi6rV68WfzHCKQAACCoyHwAAhEnNx6WC4AMAADtYXNUWAAAgKMh8AABgA4dlmcnfbYQigg8AAOxg0ewCAAAQFGQ+AACwgYPeLgAAIKgsml0AAACCgmYXAABs4KDZBQAABJUVvs0uZD4AALCBI4wzH1xYDgAABBWZDwAA7GDR7AIAAILMEaLNJv6i2QUAAAQVzS4AANjBss5P/m4jBBF8AABgAwe9XQAAAIKDzAcAAHaw6O0CAACCyOE8P/m7jVBEbxcAABBUIdXsMnLkSJk3b56sXLlSrrzySunVq5c89thjdu8WslnH/kflwf7HvObF7Y6QR5pebm4//nKcNLjxtJQofU7+OZNLtq0pKO+9VEbidufns0FIeH/WYild5p808+fNrCSTx9WR29rul2YtDkn1mvFSoGCy3HtrS0k4ndeWfUUAWTS72KJz587ywQcfyKOPPipvvfWW1zINLN58803p1KmTTJ061czr0aOHPPjgg+b2/PnzpWjRorbsN4Lvt+355en/VnXfT0lxuG/v+rWALJlVTH4/lE8KF0uWjv2PychP9kqnRleI0/nvesCl6okuN0juXP92maxU7W95aeJPsmJxGXM/In+KrFsdbabOj223cU8RSI4w7u1ie+ajQoUKMmPGDBk/frxERkaaeYmJiTJ9+nSpWLGi17rFixc3k6pUqZIt+wt7pKSI/PV7+md630wr4b597GA++eDlGHlr8U4pXeGsHNkfEcS9BC5O/Env4/Tuh/bI4YMFZNP688f23E/PB951GvzBW5yTWOE7zoftNR9XXXWVCUBmzZrlnqe3NfBo0KCBe57T6ZRRo0ZJlSpVTJBSr149+eKLL9zLv//+e3E4HPL1119L3bp1JX/+/HLdddfJ5s2bvZZnNCnNsLju586dW8qWLSuDBg0yzw17latyVqav2yJTV22TQW/sl1Llzqa7XkRkirT47wk5sj+f/H6YtDRCT548Tmne8qAsnFdBz2vt3h0gZwYfqkuXLjJlyhT3/ffff18efvhhr3U08Pjwww9N88yWLVukX79+0rFjR1m2bJnXegMHDpRXXnlFfvnlFylVqpS0adNGzp07J9dff70cOXLETDNnzjTruu7r5BIVFWXuHzhwwGRjxowZI99++22G+56UlCTx8fFeEwJr+7oCMu6JCvJsh6ry+tPlJKbiWXll9m6JLJjiXuc/nf6QObs2yZd7Nss1N/0tg++rKsnnLonDG8iS65odlUKFkmXR1xp8IByaXRx+TqHI9mYXpUHE4MGDZf/+/eb+jz/+aJpiNFvh+oHXYtNFixZJ48aNzbyqVavKihUr5O2335ZmzZq5tzV06FC59dZbzW2tJylfvrzMnj1b7r33XomJiTHzXU03rvueNOvhmq9Zlly5ckmRIkUy3HcNil544YUAvhtIbc3SKPftfdsiZfv6gvLRz1ul6R0n5dtPzqelteZj3fLCUjz6nNzd83d59u390q9tdTmXRACC0NLiP3GyZnUpOfEHBdM5nkXBqa00Q9G6dWvT7GFZlrldsmRJ9/Ldu3fLmTNn3EGFy9mzZ72aZpQrOHEFGTVr1pRt27b5vC+nTp2SQoUKSUpKigl6nn76aZM1yYgGTU8++aT7vmY+tBkJ2SchPrcc3BshZSv/2/Ry5u/cZjq8L8JkSmZu2yJNbj8l388pxkeBkFEq5ozUv+Z3GTn4art3Bcj5mQ9X00vv3r3N7UmTJnktO336tPmr9RzlypXzWhYREdiCwsKFC8u6detMEKTNO7pfDRs2lPbt26e7vj5/oPcBmctfIEXKVjori2emf/iaEh6HJXnzhWg+EmHr1tZxcuqvCPl5ZbTdu4IgcNDbxX633XabyWRos0fLli29ltWqVcv8wGsdhmcTS3pWr17t7iXz119/yc6dO+WKK67weT+0maV69ermdo0aNeTjjz82zTYZBR/Ift2ePyyrv4uS4wfzSYmYc/LggKOS4hT5fnYxiamYJM3uOClrlxWWUyfySKky5+Te3sfl7D+55OfFhfl4EDIcDktubX1QFs8vL84U7+bCYsUTpViJJClT/oy5X7lavPxzJo8cPxYpp+Pz2bTH8JsVvr1dLpnMh/YucTWP6O3U2YgBAwaYIlPteXLDDTeY5hGtDdECUR0LxGX48OFSokQJKV26tDz77LOm+aZdu3Y+74dmPI4ePWr+bt++3RS09u3bN4CvFFlVssw5GfzmfilcLEVO/ZlHtvxSUJ74Tw0TbOTOa0ntRglyZ7c/pFCRFDn5Rx7ZtLqgqfc49Se9XRA66l/zh0SX+Ue+M71cvN1+537p8Mgu9/0xb60yf8ePqCeL5tPMi9BzyQQfSgOJjIwYMcLUhmiB5969e80AY9pN95lnnvFab/To0SZY2LVrl9SvX1+++uoryZfP9zMDrdkoU6aMycBoAHPPPfeYHjSwz6ieGY/pcuJYXhny4L+DjwGhav3PpaR14/+ku2z6ezXNhJzFYUOzy/Lly2Xs2LGydu1a07NTM/ueJ+h64q0dN9555x05efKkNGnSRCZPnmxaAlxOnDghffr0Mb+v2lqgLQOvvfaaqZf0fb/1mXIA7RnTvHlz09Ri58inGrxo75hYaSt5HJx5I2fKU4mzbeRMyc4kWXTgTZNdz+yEOBC/E41vGy558vrXqyn5XKKsWvC8z/v7zTffmFYDrWW866670gQfL7/8sjnJ196i2uNzyJAhsmnTJtm6dasZP0vdfvvtJnDR3qY6lIUOjXHNNdeYwUFDMvMBAACyjwYOOqVHcxETJkyQ5557Ttq2bWvm6fha2gowZ84cue+++0x5xIIFC8xYWldffb5X1uuvvy6tWrWScePGmcE5fcEgCAAAhPggY/GpBrvUoSKyat++fabm8ZZbbnHP0wxNo0aNZNWq83VG+ldbF1yBh9L1tfnlp59+8vm5ckzwERsba6I2LjYHAAgJTisw0/+uk6aBgmvSppOs0sBDaabDk953LdO/0dHeXcHz5MljxtVyreMLml0AAAjxEU7j4uK8aj4u9fGnckzmAwCAcBUVFeU1XUzw4bq0yLFjx7zm633XMv17/Phxr+XJycmmB0x6lyzJCMEHAAA2cASi7iOA+6O9WzSAWLx4sXue1o9oLYfr0iX6V7vgalddlyVLlpgxuLQ2xFc0uwAAECYjnJ4+fdpcL82zyHTDhg2mZkNHB3/iiSfkxRdfNON6uLraag8WV3dcHTFcRyTv1q2bucq8drXVS6NoTxhfe7oogg8AAMLEmjVrzJhYLq4Lo+pI4Xpx16eeekoSEhKke/fuJsOhI4pr11rXGB9q2rRpJuC4+eab3YOMTZw4MUv7QfABAECYjHAa+7+eoRluz+EwlynRKSOaJcnKgGLpIfgAACDEe7uEGgpOAQBAUJH5AADABg7LMpO/2whFBB8AANjB+b/J322EIJpdAABAUJH5AADABg6aXQAAQFBZ4dvbhcwHAABhMsLppYKaDwAAEFRkPgAACJMRTi8VBB8AANjBotkFAAAgKMh8AABgA4fz/OTvNkIRwQcAAHawaHYBAAAICjIfAADYwWKQMQAAEESOMB5enUHGAABAUNHsAgCAHazwLTgl+AAAwA6WiPjbVTY0Yw+CDwAA7OCg5gMAACA4aHYBAMC2rraW/9sIQQQfAADYwQrfglO62gIAgKAi8wEAgB2cWnUagG2EIIIPAABs4KC3CwAAQHCQ+QAAwA5W+BacEnwAAGAHK3yDD3q7AACAoCLzAQCAHazwzXwQfAAAYAcnXW0BAEAQOehqCwAAcrrKlSuLw+FIM/Xq1cssj42NTbOsR48eAd8Pml0AAAiTmo9ffvlFUlJS3Pc3b94st956q9xzzz3ued26dZPhw4e77xcoUEACjeADAAA7OC1te/F/G1lQqlQpr/ujR4+WatWqSbNmzbyCjZiYGMlOdLUFACDExcfHe01JSUkXfMzZs2fl448/li5dupjmFZdp06ZJyZIlpXbt2jJ48GA5c+ZMwPeXzAcAACHe7FKhQgWv2UOHDpVhw4Zl+tA5c+bIyZMnpXPnzu55DzzwgFSqVEnKli0rv/76qwwaNEh27Nghs2bNkkAi+AAAwBZWAMbpOP/4uLg4iYqKcs+NiIi44CPfe+89uf32202g4dK9e3f37Tp16kiZMmXk5ptvlj179pjmmUAh+AAAIMRFRUV5BR8Xsn//flm0aNEFMxqNGjUyf3fv3k3wAQBAyLPsG+F0ypQpEh0dLa1bt850vQ0bNpi/mgEJJDIfAADYwamBQ3B7u5iHOJ0m+OjUqZPkyfNvGKBNK9OnT5dWrVpJiRIlTM1Hv379pGnTplK3bl0JJIIPAADCyKJFi+TAgQOml4unfPnymWUTJkyQhIQEU8Tavn17ee655wK+DwQfAADYwXKen/zdRha1aNFCrHSaazTYWLZsmQQDwQcAAHawuKotAAAIg5qPSwEjnAIAgKCi2QUAADtYNLsAAICgBh8SgHE+JCTR7AIAAIKKZhcAAOxAswsAAAgqp47R4QzANkIPzS4AACCoaHYBAMAOFr1dAAAAwUdQ0OwCAACCimYXAADs4Azf4dUJPgAAsIFlOc3k7zZCEcEHAAB2FZw6/R3hNDQzH9R8AACAoCLzAQCAHawA1HyEaOaD4AMAADs4nSIOP2s2QrTmg2YXAAAQVGQ+AACwg0WzCwAACGbs4XSK5QjPrrY0uwAAgKCi2QUAADtYNLsAAIBgcloijvDsakuzCwAACCqaXQAAsIOlWQtnWGY+CD4AALCB5bTE8rPZxSL4AAAAWYg+xP/MB11tAQAALohmFwAAbGDR7AIAAIIcfUi4NruQ+QgwV/FPspzz+0rJwCXLmWT3HgDZItl5NmiFnMkB+J0w2whBBB8B9vfff5u/K2R+oDcNXDoO2L0DQPZ/lxcpUiRbtp0vXz6JiYmRFUcD8zuh29JthhKHFar9dC5RTqdTDh8+LIULFxaHw2H37uR48fHxUqFCBYmLi5OoqCi7dwcIOI7x4NKfRA08ypYtK7lyZd84nImJiXL27Pksi7808MifP7+EEjIfAaYHa/ny5QO9WVyABh4EH8jJOMaDJ7syHp7y588fcgFDIDG8OgAACCqCDwAAEFQEHwhpERERMnToUPMXyIk4xpETUXAKAACCiswHAAAIKoIPAAAQVAQfAAAgqAg+AMBGI0eOlOuvv97cvvLKK+XNN9/k80COR/AB2+3cuVNKly4tPXv29Jr//fffm1FiT5486TUqYPPmzaVBgwZe84FLRefOnc1x26NHjzTLevXqZZbpOi663qeffmpuz58/Xzp06BDU/QXsQPAB21122WWyYMEC+eSTT2Tw4MEZrpecnCz33HOPGb7+22+/laJFiwZ1PwFf6ZD/M2bMkH/++ccrcJ4+fbpUrFjRa93ixYub9VWlSpWCMromYDeCD1wSNJPx9ddfy8SJE2XMmDHpXjOnU6dOsnHjRlm4cKFER0eb+ZUrV5YJEya411u8eLE5s2zXrp17XmxsrJmnU2RkpNSvX98EO0B2ueqqq0xAMWvWLPc8va2Bhx7rnsf1qFGjpEqVKubYrFevnnzxxRdpsn/6b6Nu3bpmOO7rrrtONm/e7LU8o0lNnTrVfT937tzmmiWDBg0yzw3YheADl4wmTZqYL+ghQ4bI22+/nSZdrUHHokWL0pw5uuiXaf/+/aVQoUJplnXr1k2OHDlivrRr165tAhkgO3Xp0kWmTJnivv/+++/Lww8/7LWOBh4ffvihvPXWW7Jlyxbp16+fdOzYUZYtW+a13sCBA+WVV16RX375RUqVKiVt2rSRc+fOmVoRPa51mjlzplnXdV8nz+vC6P0DBw7I+PHjTYCv2UPALlxYDpcUTTlrENGnTx/z5a20Kebdd981X6CZNbV88MEHkpSUJG3btpXTp097LStQoIC57LQ23WjWhNQ2spsGEXrs7t+/39z/8ccfTVOMZiuUHqtabKoBdePGjc28qlWryooVK0zw3axZM/e2dBTfW2+91X2c68UrZ8+eLffee685rl3NN8p135NmPVzzNcuiF8Dk3wDsRPCBS4ZeXvqRRx4xBXiaftZshevLVptTnnvuOendu7d89tlnaR575swZs1zPIF1ngJ60B4EGMPqFrwHMl19+GZTXhPClGYrWrVubZg+9TLveLlmypHv57t27zXHrCio8/x14Ns0oV3DiCjJq1qwp27Zt83lfTp06ZTKCKSkp5t/A008/7e5hA9iB4AOXjJdeeslkLDQVrV+UmiZ+/vnnTU+Apk2bmuBBg5K5c+ea7IansWPHmi9kTUenF3xoD4Jnn33WFP1pMKOFq1u3bjXZFCC7aPZOA2Y1adIkr2Wu7JzWc5QrV85rWaCvVVS4cGFZt26dCYK0eUf3q2HDhtK+ffuAPg/gK2o+cEnQWozRo0ebdLOrZuPGG2/0+qu9YjT9/Nhjj5kzORcNUrQ9XKeMaIq5evXqpt5Dt3Ho0CH5+eefs/11IbzddtttJpOh9RktW7b0WlarVi0TZGgdhh6bnpOr94vL6tWr3bf/+usv0z39iiuu8Hk/tJlFt1ujRg1TjH3TTTeZZhvALmQ+YDut8dDmlvvuuy/NF3RqAwYMMM0u+vedd95xn1HqGVzqVLUnTW8fPXrUpJw185EnTx7zZQxkJ+1d4moe0dupsxF6HGuRqf4buOGGG0xQrbUhmpHzLIoePny4lChRwoyHoxk8bb7x7NF1IZrx0ONf/27fvt0UtPbt2zeArxTIGoIP2O61116T3377zQywdCEaNGivgWuvvVYeeOABM0+/uLXJJjMaqOiUL18+c/Y3bdo0000XyG6ZNe2NGDHC1IZoU+PevXtNPZJ2033mmWe81tOsoAYLu3btMl3Fv/rqK3Ms+yo+Pl7KlCljCk81gNFmR+1BA9jFYWkoDAC45GjPGB3RV5taGFQPOQk1HwAAIKgIPgAAQFDR7AIAAIKKzAcAAAgqgg8AABBUBB8AACCoCD4AAEBQEXwAOVDnzp29RsCMjY2VJ554wpZxKnRgq5MnT2a4ji6fM2eOz9scNmyYGWjLHzqonT7vhg0b/NoOgItD8AEEMSDQHzyddHRKHd5dh81OTk7O9ueeNWuWGU0zUAEDAPiD4dWBIF9obMqUKeYaMzqcfK9evSRv3rwyePDgNOvqBcmyMoR2ZvQy7ABwqSDzAQSRXsU0JiZGKlWqJD179pRbbrlFvvzyS6+mEr1OTdmyZaVmzZpmflxcnNx7771meG0NItq2bWuaDVxSUlLkySefNMv14mNPPfWUuYCYp9TNLhr8DBo0yFw9VfdJszDvvfee2a4O562KFStmMiC6X65r6Og1SKpUqSKRkZFSr149+eKLL7yeRwMqvfqwLtfteO6nr3S/dBsFChSQqlWrypAhQ8xVYVPTKyDr/ut6+v54XulYvfvuu+bKr/nz55fLL79c3nzzzSzvC4DsQfAB2Eh/pDXD4bJ48WLZsWOHLFy4UObNm+e+FLteAfWHH34wVzwtVKiQ+1Lt6pVXXpGpU6eaC+6tWLFCTpw4ccHLpT/00EPyySefyMSJE81VV/WHXLerP+YzZ8406+h+HDlyxFz4T2ng8eGHH8pbb70lW7ZsMVdj7dixo7lCqitIuuuuu6RNmzamlkKvVPz0009n+T3R16qvZ+vWrea59YKA48eP91pn9+7d5urGeoG1BQsWyPr16+Wxxx5zL9cLBz7//PMmkNPXN3LkSBPE6BWNAVwC9MJyALJfp06drLZt25rbTqfTWrhwoRUREWENGDDAvbx06dJWUlKS+zEfffSRVbNmTbO+iy6PjIy0vv32W3O/TJky1pgxY9zLz507Z5UvX979XKpZs2ZW3759ze0dO3ZoWsQ8f3qWLl1qlv/111/ueYmJiVaBAgWslStXeq3btWtX6/777ze3Bw8ebNWqVctr+aBBg9JsKzVdPnv27AyXjx071mrYsKH7/tChQ63cuXNbBw8edM/75ptvrFy5cllHjhwx96tVq2ZNnz7dazsjRoywGjdubG7v27fPPO/69eszfF4A2YeaDyCINJuhGQbNaGgzxgMPPGB6b7jUqVPHq85j48aN5ixfswGeEhMTZc+ePaapQbMTjRo1ci/LkyePXH311WmaXlw0K5E7d25p1qyZz/ut+3DmzBm59dZbveZr9qVBgwbmtmYYPPdDNW7cWLLq008/NRkZfX2nT582BbmpL0tfsWJFKVeunNfz6Pup2Rp9r/SxXbt2lW7durnX0e0UKVIky/sDIPAIPoAg0jqIyZMnmwBD6zo0UPBUsGBBr/v649uwYUPTjJBaqVKlLrqpJ6t0P9TXX3/t9aOvtGYkUFatWiUdOnSQF154wTQ3abAwY8YM07SU1X3V5prUwZAGXQDsR/ABBJEGF1rc6aurrrrKZAKio6PTnP27lClTRn766Sdp2rSp+wx/7dq15rHp0eyKZgm0VkMLXlNzZV60kNWlVq1aJsg4cOBAhhkTLe50Fc+6rF69WrJi5cqVphj32Wefdc/bv39/mvV0Pw4fPmwCONfz5MqVyxTpli5d2szfu3evCWQAXHooOAUuYfrjWbJkSdPDRQtO9+3bZ8bhePzxx+XgwYNmnb59+8ro0aPNQF3bt283hZeZjdFRuXJl6dSpk3Tp0sU8xrVNLeBU+uOvvVy0iej33383mQRtyhgwYIApMtWiTW3WWLdunbz++uvuIs4ePXrIrl27ZODAgab5Y/r06aZwNCtq1KhhAgvNduhzaPNLesWz2oNFX4M2S+n7ou+H9njRnkRKMydaIKuP37lzp2zatMl0cX711VeztD8AsgfBB3AJ026ky5cvNzUO2pNEswtay6A1H65MSP/+/eXBBx80P8Za+6CBwp133pnpdrXp5+677zaBinZD1dqIhIQEs0ybVfTHW3uqaBahd+/eZr4OUqY9RvRHXfdDe9xoM4x2vVW6j9pTRgMa7YarvWK0l0lW3HHHHSbA0efUUUw1E6LPmZpmj/T9aNWqlbRo0ULq1q3r1ZVWe9poV1sNODTTo9kaDYRc+wrAXg6tOrV5HwAAQBgh8wEAAIKK4AMAAAQVwQcAAAgqgg8AABBUBB8AACCoCD4AAEBQEXwAAICgIvgAAABBRfABAACCiuADAAAEFcEHAAAIKoIPAAAgwfT/rwC/5mPuM0sAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "ConfusionMatrixDisplay.from_predictions(\n",
    "    y_test,\n",
    "    logreg_pred,\n",
    "    display_labels=[\"Жив\", \"Мёртв\"]\n",
    ")\n",
    "\n",
    "plt.title(\"Матрица ошибок Logistic Regression\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49f3bc2e",
   "metadata": {},
   "source": [
    "# Dummy baseline\n",
    "\n",
    "Сначала построим простую базовую модель, с которой будем сравнивать обученные алгоритмы. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f5e66dcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "dummy = DummyClassifier(strategy='prior')\n",
    "dummy.fit(X_train, y_train)\n",
    "\n",
    "dummy_pred = dummy.predict(X_test)\n",
    "dummy_prob = dummy.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd11b6a",
   "metadata": {},
   "source": [
    "# Random Forest\n",
    "\n",
    "Используем 300 деревьев и балансировку классов, а предобработку оставим той же, чтобы корректно работать с пропусками и категориями."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "728667ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = Pipeline([\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('model', RandomForestClassifier(n_estimators=300, random_state=42, class_weight='balanced'))\n",
    "])\n",
    "\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "rf_pred = rf.predict(X_test)\n",
    "rf_prob = rf.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "051934c6",
   "metadata": {},
   "source": [
    "# Сравнение моделей\n",
    "\n",
    "Создадим общую функцию расчёта метрик, чтобы оценивать все модели одинаково. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2ba36a02",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import (\n",
    "    accuracy_score,\n",
    "    precision_score,\n",
    "    recall_score,\n",
    "    f1_score,\n",
    "    roc_auc_score\n",
    ")\n",
    "\n",
    "def calculate_metrics(y_true, y_pred, y_prob):\n",
    "    accuracy = accuracy_score(y_true, y_pred)\n",
    "    precision = precision_score(y_true, y_pred)\n",
    "    recall = recall_score(y_true, y_pred)\n",
    "    f1 = f1_score(y_true, y_pred)\n",
    "    roc_auc = roc_auc_score(y_true, y_prob)\n",
    "\n",
    "    return {\n",
    "        \"accuracy\": accuracy,\n",
    "        \"precision\": precision,\n",
    "        \"recall\": recall,\n",
    "        \"f1\": f1,\n",
    "        \"roc_auc\": roc_auc\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b3effb72",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Masha\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1879: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", result.shape[0])\n"
     ]
    }
   ],
   "source": [
    "dummy_metrics = calculate_metrics(y_test, dummy_pred, dummy_prob)\n",
    "logreg_metrics = calculate_metrics(y_test, logreg_pred, logreg_prob)\n",
    "rf_metrics = calculate_metrics(y_test, rf_pred, rf_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e0278d44",
   "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>accuracy</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>roc_auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dummy</th>\n",
       "      <td>0.745</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.682</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0.573</td>\n",
       "      <td>0.478</td>\n",
       "      <td>0.706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.717</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0.739</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     accuracy  precision  recall     f1  roc_auc\n",
       "Dummy                   0.745      0.000   0.000  0.000    0.500\n",
       "Logistic Regression     0.682      0.410   0.573  0.478    0.706\n",
       "Random Forest           0.717      0.452   0.532  0.489    0.739"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_table = pd.DataFrame([dummy_metrics, logreg_metrics, rf_metrics], index=['Dummy', 'Logistic Regression', 'Random Forest'])\n",
    "\n",
    "results_table.round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea217d90",
   "metadata": {},
   "source": [
    "Dummy показывает высокую accuracy (0.745), потому что всегда выбирает преобладающий класс «жив», но не находит ни одной смерти: recall и F1 равны нулю, ROC AUC — 0.5. Случайный лес даёт лучший ROC AUC (0.739) и F1 (0.489), а логистическая регрессия немного лучше находит умерших по recall (0.573 против 0.532). В целом случайный лес выглядит сильнее."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cb45f12",
   "metadata": {},
   "source": [
    "Матрица ошибок покажет, на каких классах случайный лес ошибается чаще и какие ошибки скрываются за общими значениями метрик."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bb67ca77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAHHCAYAAAAf2DoOAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAUJpJREFUeJzt3Qd4FOXWwPGzCSSEEjqE3qVIFRURpVgochGUi1dFBUEQBAQEBKSDAh+oYEGwUK8gNhBFLkpHBJEiKlU6QYpeaQImJNn5nvNyd91NY8NudrPZ/89nzO7M7OxsYefMec/7js2yLEsAAAD8JMxfTwQAAEDwAQAA/I7MBwAA8CuCDwAA4FcEHwAAwK8IPgAAgF8RfAAAAL8i+AAAAH6Vw79PByAruHLlipw5c0bsdruULFky0LsDIMSQ+QBCxNatW+XRRx+VIkWKSGRkpJQoUULat28f6N3KMpo2bWomAJmP4ANOc+bMEZvNZqYNGzakeGd0JP4yZcqY5f/4xz9454LIkiVL5I477pDdu3fLSy+9JCtWrDDT22+/7fd9GT16tPN7plPOnDmlfPny8uyzz8q5c+f8vj9ZVfL3yXWaMWOGZDWXL182+7x27dpA7wqCAM0uSCFXrlyyYMECc7BytW7dOjl+/Lg5a0bw0OaVp556Slq0aCEff/yxRERESFYwffp0yZs3r1y6dElWrVolb7zxhmzfvj3VwDeUOd4nVw0aNJCsGHyMGTPG3CaDhGsh+EAK9913nzlIvf7665Ijx99fEQ1I6tevL//9739514LI7NmzJS4uzmS2skrgof75z3+aJiD19NNPy8MPPywffvihfP/993LrrbcGevey5PvkSxr05cmTx+fbBTxBswtSeOSRR+SPP/4waXnXAsVPPvnE1Ayk5uWXX5bbb79dChcuLFFRUSZI0fVdpZVCdkyOsyVN2+p9PRC98MILEhMTY34k77//fomNjb1mO/2WLVuc20z+/L17906x79qEpGl/Vz/99JN07txZKlasaDJBug9dunQx74snfvvtN+natasUL17cPL5OnToyd+5ct3WOHDli9knfO1c1a9ZM8ZqGDx9u1r148aLb69E0t6vJkye7vZfqu+++k7p168r48eNNs5lmrqpUqSITJ040BaeuEhMTZdy4cVKpUiWznr4v+hnEx8e7rafz9f1x1b17d/Narzftfuedd5q/Bw8edMvaDBw4UGrVqmXO/qOjo6VVq1by448/uj3W8Z356KOPTLNS6dKlzb7cfffdcuDAgRTP9c4775jXqN9VDXS++eYbn3yO06ZNM9+Z3LlzS/Pmzc33VZsr9T3VfdLna9u2rXldvqInCvrvTbetQcpjjz0mv/76q9s6+lnp+6fvrZ5c5MuXTzp27GiW6Xdg6tSpcuONN5rXqK9Vg8GzZ8+mqBnS7Jk+hz5XhQoVzL8Jx3tQtGhRc1uzH45/f8m/n4ADmQ+koAeWhg0bygcffGB+6NV//vMfOX/+vDk71YxIcq+99poJDvQHTQOVhQsXSocOHWTp0qXSunVrs86///1v5/r6Y68HgClTpjjP6vRHz5UeRPQHbPDgweYgoD+Q99xzj+zYscP8+KVF1/eWBl6HDh2SJ5980gQeu3btMvurf/VgnjywcfXXX3+Zg78e9DTY0R9pPUDoAUBrGvr27SuZQbc9YcKEFPM1YNKmDJ30YKEHKm3mGDp0qDlouNYPaPOMHlz1bHvAgAGyefNms809e/bI4sWL03zuUaNGycyZM03AeL0pd90XVbBgQec8/Qw+++wz813S9/H06dOmTqVJkyamfiV5Tx0NqMLCwkzAot/XSZMmme+kvg4H3U89uGqw3K9fP/Mc+t0tVKiQCc6u93OcP3+++e736dPHBBf63A899JDcddddJjjS76VuS5uXdP9mzZrl0fuSPFAJDw93vkeazdLv6C233GI+J31/9N/it99+Kz/88IMUKFDALbDU4EGbUzVQ0gBJ6Xvh2I7W3Rw+fFjefPNN83jdjtbk6L8/DaY0wBgyZIjZrn5eixYtMtvQ+do81LNnT3nggQfkwQcfNPNr167t0WtECLKA/5k9e7alX4ktW7ZYb775ppUvXz7r8uXLZlmHDh2sZs2amdvlypWzWrdu7fa+OdZzuHLlilWzZk3rrrvuSve5Dh8+nGLZmjVrzLJSpUpZFy5ccM7/6KOPzPzXXnvNOa9JkyZmcli2bJlZp2XLluavK73fq1evFM+nr0VfU3qvR33wwQdmG+vXr0/3OzN16lSz3vvvv+/2fjRs2NDKmzev8zXpa9f1Jk+e7Pb4G2+80e01qWHDhpl1//zzT7fXM2rUKOf9559/3ipWrJhVv359t8frbV139OjRbtvs3Lmzmf/zzz+b+zt27DD3n3rqKbf1Bg4caOavXr3aOU/fr06dOpnbb7/9tln+xhtvWJ7Qfdb19+3bZ/3+++/WkSNHrFmzZllRUVFW0aJFrUuXLjnXjYuLs5KSktwer+9bZGSkNXbs2BTfmerVq1vx8fHO+fpdcX2N+jnoe1S3bl239d555x2znuv7ltHPUff93LlzznWHDh1q5tepU8dKSEhwzn/kkUesiIgI89o8eZ+ST47vquO16L+zv/76y/m4pUuXmvVGjhzpnKeflc4bMmSI23N88803Zv78+fPd5i9fvtxt/uLFi52/DWnRzzL5dxJIC80uSJWesemZn2Yu/vzzT/M3rSYX5ZqJ0HStnnVqGl0LCK/XE088YdLDDno2rt1Dly1bllYgbc7mtfuotwV5rq9H6yW0zuW2224z96/1mnT/NFuizVcOevaoZ5XabKKFu76maXY9ox4xYkSK4kTH2XL//v3d5mlmQ3355ZfO/VbPPfdcuusl70XzzDPPyKBBg1Jt0kpP1apVzRmzZto0I1O5cmWTYXOckStt+tFMhkpKSjJZHH19+tjUPgc9e3eta3E05Wh2w9F0oGfxPXr0cFtPsxn58+f36nPU7IzrNhzfQW0Gca2d0vmaIUneNJKWTz/91Nk7SSfNsLi+Fn3/tbnEQTON1apVS/Xz0syEK83k6D7fe++95jvumDQ7pu/zmjVrzHqODIr+DiQkJHi030B6aHZBqvSgoE0cWmSqVez6w68H/7Toj9KLL75omkRc6wPSa564Fq1LcKXb0gOUIz2fnP4oa7OItvvrfntDU93adq3NR/oD70oDq/QcPXrU7LvjoOlQvXp153Jf02YPbYLQFHpqtTa6TOslXOkBXPfR8X7qful9fY9d6QFYDz7J91s/a32v9btxPTUMelDVffr9999NU56m+5M3p2k9gjYjvPXWW2a5PpeD1hclV7ZsWbf7juYJR/2C4zUk/25pUKG1Gt58jsmf2xGIuDbluM5PXlORlsaNG6dacOp4fv0ck9PgI3mvIQ2AtO7E1f79+833uVixYqk+t+O7r81cGtTrvwltKtXmqHbt2pkTEnq/4XoQfCBN+sPSrVs3OXXqlKn9cG0/dqX1G9pmrj+SepDQ7IT+mGsvC2+DAE/pmaSe9Wtx4A033OCTzM/GjRvNGb0Wa+pZoB4IW7ZsmaJIM9C0HkPb7N9//33zvieXXn1MajwNGLXoU78XWtSp75Oe4Wek3sP1oNqmTRtTVKr1Gdu2bXMe8LVIVj9XzYxo0abWZegyrdVI7XPQDE9qrrZSZa60njuQ++TKNYvkoO+hBh6ObEpyjiJS/U5oUKv1Tl988YV89dVX5jN55ZVXzLzUsm1Aegg+kCYtHNMzaf1x0ULC9M5gNe2rP0iuZ0EafHhDz8qS/1hrwV5qRWwa9OhZmi+q6/WMVAsy9Sxv5MiRae5PWsqVK2d6y+gPu+uP/d69e53LfUmbmjRA+te//pXqci2U/Prrr03zmWsz1i+//GL20dHTR/dL7+vrdJzdKy1i1ALL5PutwYKm7TW40b/a20Vft2sTgKf04KXZG2020WyKFjYrPeA1a9bMFIm60v25nu6njtegr1ELQR20KUEzK9qbJVCfY0Y5nn/fvn1ur8Uxz5P90x4/K1eulEaNGnkUpGrTo05aDK4nFhosanZQC5W9yXIi9FDzgXQPCFrBrgd0PTNNi57Z6Q+Pa0pcU/naS8Eb8+bNMwdMBz0QnTx50tkDx0HX0R9DrWnQJgJvOc5Uk5+Zam8bT2hXRs0WuQZs2tNAazL0PdUUtq9s2rTJ1F1oL4+0fvx1f/Sz0R4Mrl599VXz19EbSddL7XUmX8/hpptuMl2g9cD83nvvmc987Nix1/1a9ECmzQL/93//5/ZZJP8cNNDxtF4iuZtvvtmczWsPH82WOWjmKPnoqv78HK/3tWjWQl+La1On1s1oNiz555VWhk+/G5pVSk5fq+M90YA8+eegAa9yPLejVodRauEJMh9IV6dOna75DumPnB6gtElCm2o0A6HjHWjtgJ45Xi9NsWu3QD0b1rNvPSjqNrUpyJUWHupZ8PPPP3/NbR47dkyWL1/uNk9rDrS4VufrAUXrELRJQLtK6hlxqVKlTOZAz4w9oRkA7Q6qRYzahKCZBQ2ctNuivgbX7IPjLNV1n7SYUQ/orvMcBZPJ6X5psaDW56RFD6K6fNiwYeY16EFj9erVJmOlhZc6rojSs379vLVLsR5A9L3QAb+0662272sGIi26De1KqkGQZi2up4ulNhlp91VtwtHXrt8nHYNFAxr9DmjX2J9//tk0ESSvz8jIc2htkmb0NFug2SJ9TzRLl3ybGf0c/U1fiwZq+t7oZ6WFsY6utrqvyQuMU6OP0/dCu+lqDY92p9XtamZIgzzdltZ66XdAs4uaDdVsiQb87777rvm34ghaNXNSo0YNE6xp06f++9XvheP7BbhJsx8MQrqrbXpS62o7c+ZMq0qVKqYLZLVq1cy2HF0Fr7errXZt1e6K2p1Qu2Hqcx49etRtXUc30ilTprjNT+25U+u2mHxy7M/x48etBx54wCpQoICVP39+09X4xIkTHnclPH36tPXkk09aRYoUMd0qa9WqZV6zK0cXzYxMybva2mw2a9u2bSnek+RddS9evGj179/fKlmypJUzZ06rcuXK1sSJE1N0Y9UuoWPGjLEqVKhg1itTpoz5DJJ3C3Xtauug6+hnf8stt1iJiYlpvjeOz0a7ZiZ3/vx583479l+3OWDAAKtEiRLmO9CoUSNr06ZNKV6j4zvz8ccfp/oeJ3/v33rrLfMa9ft68803m+7Tqb1vGfkck3eZTmufPP13lt775OrDDz+06tWrZ15LoUKFrI4dO5rvryv9rPLkyZPmNrSrsXbR1vdYu9jr69Su2/qdV9u3bzddhMuWLWueR/9N/uMf/7C2bt3qtp2NGzea7eh7RbdbpMem/3MPR4DA0gGZ9Cxbz7zS62HjS9pkoLURehacfLRTAIBvUfMBAAD8iuAD+F97tQ49ndFuqQCAjKPgFPjfdWWSF6ICADIHNR8AAMCvaHYBAAB+RfABAAD8ipoPH9OhmE+cOGEGIGK4YQAILjr6hA6iphdjTH4tHF+Ki4tzG2XXG3qF5uu5rEEgEXz4mAYeya9iCQAILrGxsSmuAuzLwKNCubxy6re/L0nhDb2shI5RFEwBCMGHjzmGXD66vbxE56VVC9lThxbXvm4IEIwS7Vdk7dG3M3X4/CtXrpjA4+i28hKdz7vjxIU/7VKu/hGzTYKPEOZoatHAw9svFZBV5Qj7++rFQHbkj2bzvPlsZvKGXYLzasJkPgAACIAkyy5JlvfbCEYEHwAABIBdLDN5u41gRLsAAADwKzIfAAAEgN385/02ghHBBwAAAZBkWWbydhvBiGYXAADgVwQfAAAEsODU7uXkqQkTJsgtt9xixjApVqyYtGvXTvbt2+dcfuTIEdPFOLXp448/dq6X2vKFCxdm6LUTfAAAEAB2sSTJyykjwce6deukV69e8t1338mKFSskISFBmjdvLpcuXTLLdXTukydPuk1jxoyRvHnzSqtWrdy2NXv2bLf1NJDJCGo+AAAIAcuXL3e7P2fOHJMB2bZtmzRu3FjCw8PNUO2uFi9eLA899JAJQFwVKFAgxboZQeYDAIAgb3a5cOGC2xQfH3/N5z9//rz5W6hQoVSXa1CyY8cO6dq1a4plmkEpUqSI3HrrrTJr1ixzQb6MIPMBAECQ93Ypk+yCpqNGjZLRo0enewX2fv36SaNGjaRmzZqprjNz5kypXr263H777W7zx44dK3fddZfkzp1bvv76a3nmmWfk4sWL8uyzz3q83wQfAABkg6vwRkdHO+9HRqZ//SXNXOzcuVM2bNiQ6vK//vpLFixYICNGjEixzHVevXr1TM3I5MmTMxR80OwCAEAA2H00KQ08XKf0go/evXvL0qVLZc2aNVK6dOlU1/nkk0/k8uXL8sQTT1zzdTRo0ECOHz/uUVOPA5kPAAACIOl/PVa83YantC6jT58+poh07dq1UqFChTTX1SaX+++/X4oWLXrN7WpdSMGCBa+ZbXFF8AEAQAAkWVcnb7fhKW1q0aaUJUuWmLE+Tp06Zebnz59foqKinOsdOHBA1q9fL8uWLUuxjS+++EJOnz4tt912m+TKlct02R0/frwMHDgwQ/tN8AEAQAiYPn26+du0adMUY3Z07tzZeV97r2hzjI4BklzOnDll2rRp0r9/f5NJqVy5srz66qvSrVu3DO0LwQcAAAFgd6nZ8GYbnvK0O6xmMnRKTcuWLc3kLYIPAAACwC42SRKb19sIRvR2AQAAfkXmAwCAALBbVydvtxGMCD4AAAiAJB80u3j7+ECh2QUAAPgVmQ8AAAIgKYQzHwQfAAAEgN2ymcnbbQQjml0AAIBfkfkAACAAkmh2AQAA/g0+wszk3TaCE5kPAAACwPJBzYduIxhR8wEAAPyKzAcAAAGQRM0HAADwa/BhhZnJu21IUKLZBQAA+BXNLgAABIBdbGL3Mgdgl+BMfRB8AAAQAEkhXPNBswsAAPArMh8AAARtwaklwYjgAwCAgNV82LzeRjCi2QUAAPgVmQ8AAALA7oNru9DbBQAAeCyJmg8AAODvzIc9RDMf1HwAAAC/ouYDAIAASLJsZvJ2G8GI4AMAgABI8kHBaRLNLgAAANdG5gMAgACwW2Fm8m4bwVlwSvABAEAAJNHsAgAAsrMJEybILbfcIvny5ZNixYpJu3btZN++fW7rNG3aVGw2m9vUo0cPt3WOHTsmrVu3lty5c5vtDBo0SBITEzO0L2Q+AAAIALsPeqvoNjy1bt066dWrlwlANFh44YUXpHnz5rJ7927JkyePc71u3brJ2LFjnfc1yHBISkoygUdMTIxs3LhRTp48KU888YTkzJlTxo8f7/G+EHwAABC0g4yFebzu8uXL3e7PmTPHZC62bdsmjRs3dgs2NLhIzddff22ClZUrV0rx4sWlbt26Mm7cOBk8eLCMHj1aIiIiPNoXBhkDACDIXbhwwW2Kj4+/5mPOnz9v/hYqVMht/vz586VIkSJSs2ZNGTp0qFy+fNm5bNOmTVKrVi0TeDi0aNHCPOeuXbs83l8yHwAABO21XcLM3zJlyrjNHzVqlMlEpMVut0u/fv2kUaNGJshwePTRR6VcuXJSsmRJ+emnn0xGQ+tCFi1aZJafOnXKLfBQjvu6zFMEHwAABIBdbGbydhsqNjZWoqOjnfMjIyPTfZzWfuzcuVM2bNjgNr979+7O25rhKFGihNx9991y8OBBqVSpkvgKwQcAAEGe+YiOjnYLPtLTu3dvWbp0qaxfv15Kly6d7roNGjQwfw8cOGCCD60F+f77793WOX36tPmbVp1Iaqj5AAAgBFiWZQKPxYsXy+rVq6VChQrXfMyOHTvMX82AqIYNG8rPP/8sv/32m3OdFStWmMCnRo0aHu8LmQ8AAIJ2kLEwj9fVppYFCxbIkiVLzFgfjhqN/PnzS1RUlGla0eX33XefFC5c2NR89O/f3/SEqV27tllXu+ZqkPH444/LpEmTzDaGDx9utn2tph5XBB8AAASA3bKZydtteGr69OnOgcRczZ49Wzp37my6yWoX2qlTp8qlS5dMEWv79u1NcOEQHh5ummx69uxpsiA6PkinTp3cxgXxBMEHAAAh0uySHg02dCCya9HeMMuWLRNvEHwAABAAdh80u3g7SFmgEHwAABC0V7UNk2AUnHsNAACCFpkPAAACIElsZvJ2G8GI4AMAgACw0+wCAADgH2Q+AAAIgCQfNJvoNoIRwQcAAAFgD+FmF4IPAACC/MJywSY49xoAAAQtMh8AAASAJTaxe1nzodsIRgQfAAAEQBLNLgAAAP5B5gMAgACwWzYzebuNYETwAQBAACT54Kq23j4+UIJzrwEAQNAi8wEAQADYaXYBAAB+DT4kzEzebiMYBedeAwCAoEWzCwAAAZBk2czk7TaCEcEHAAABYKfmAwAA+JPlg6va6jaCUXDuNQAACFo0uwAAEABJYjOTt9sIRgQfAAAEgN3yfnh03UYwotkFAAD4FZkPZDkL3ygm3y4rILEHIiUil11q3HxZug47IWUqxzvXOfNbDnlvXEnZvj6fXL4YJmUqxcvDfU/Lna3PO9cZ1amCHNwVJef+yCH58idJvTv/NNspHJMYoFcGpC4szJJHu+yVZs2PS8HCcXLmv7lk5bKysnDuDSLOtLolj3XdKy3aHJU8+RJkz8+FZNrLdeTE8by8rUHK7oOCU28fHyhBtde//PKLFC9eXHr27Ok2f+3atWKz2eTcuXPOeXFxcdKsWTOpV6+e23xkfT9tyittOv9Xpi7dLxMWHpSkRJEXHqkkcZf//rpOfrasxB6MlNFzDsvbq/dJo/vOy/iny8uBn6Oc69RpdFGGvX1EZn6zR4a/e1hOHImUcd0qBOhVAWn7Z8f9cl+7IzJjSi3p0fFumT39Rmnfcb+0+echl3UOmPsacDzXvbHE/ZVDxr26SXJGJPHWBim72HwyBaOgCj5uuOEGWb58uXzwwQcydOjQNNdLTEyUDh06yIkTJ+Srr76SAgUK+HU/4Z3xCw5J83+dkfJV46TSjXEyYOox+e3XCNn/09+Bxe6teaRtl/9KtXqXpUS5K/Jov9OSJ3+S2zoPdv9dqte/LMVLJ8iNt1yWf/U+LXu355bEBD4hZC3Va56RzRtiZMumGPntVG75dm1J+eH7YlK1uuPEyZK2HQ7Kh/OqyncbSsiRg/nllRdvkkKF46ThnScDvPdANg8+lGYyvvzyS3n99ddl0qRJKZbb7Xbp1KmT/Pjjj7JixQopVqyYmV++fHmZOnWqc71Vq1aZbEm7du2c85o2bWrm6RQVFSV169Y1wQ4C69KFcPM3X4G/z/Bq3HxJ1n1eQC6cDRe7XWTtZwXkSpxNat9+MdVt6HqrFxU0j8uR02+7Dnhkz85CUqf+71KyzNXvb4XK56VG7TOy9burv18xJS9LoSLxsmNLUedjLl/KKft2F5RqNc/yLgf5CKdJXk7BKOiCD9WoUSNZtGiRjBgxQt5++223Zb169TJBx8qVK6Vs2bKpPl4DlAEDBkjevCnbSrt16yYnT56UnTt3Ss2aNU0gg8DRwGLGqFJy4y0XpXy1OOf8YW8flaQEm3S4sZb8o3wdeW1wGRk184iUqnDF7fHvvVhC7q9Uy6z3+4kIGT37cABeBZC+j9+vIutXlZK356+SJWs/l9dnrZUlH1WUtSvKmOUFC12tdzp7NtLtcefORkrBQn//u0Bw1nzYvZw8NWHCBLnlllskX7585sRcT7737dvnXH7mzBnp06ePVK1a1ZyA6zH02WeflfPn/66lU46TdNdp4cKFoVFwmj9/fhNE6BvVpUsXM0+bYt577z2Jjo5Ot6ll7ty5Eh8fL23btpWLF93PlHPnzi0xMTGm6UY/HH2e9Oh2dHK4cOGC168Nf3vzhdJydG+UvPLZfvfPcFKMXLwQLhM/PCDRhRJl0/L88lKP8vLK4v1SofrfP8Ydev4mLR85I6eP55T5r8bI5L5lZey8w2ILzpMFZFN33vWrNL33uEweU1+OHo6WilXOS/dnfzaFp6uWp34SBWTUunXrzAm6BiB6jHvhhRekefPmsnv3bsmTJ48pVdDp5Zdflho1asjRo0elR48eZt4nn3zitq3Zs2dLy5YtnfczWt4QlMHHlStX5KmnnjJvSp06dUy2whFUaHPK8OHDpXfv3vLRRx+leOzly5fN8hkzZsinn36aYvlbb71lAhgNKPTN/Pzzz68ZSY4ZM8aHrw4Ob75QSjaviJZXFh+QoiX/LtQ4cSRCPp9dVN5es9fUhSitDfl5c175fE4R6ft/x53r5i+cZKbSleKlbJWj8tjNN8qebblNDxogq+jyzC75eL5mP0qb+0cPRUuxmMvS4fH9Jvg4e+ZqxqNgwXg5+0cu5+MKFIyXQwfSP0FC1mXXglFvx/nIQMFp8jKCOXPmmJPsbdu2SePGjU223/W4WKlSJXnppZfkscceM8FKjhx/hwx6fNQT9ZBqdtE3QzMWeuDXIGTs2LFm/ocffmjeQA0evvjiC1myZEmKx06ePNmklNq0aZPqtjt27Cg7duyQH374QTp37mwKV9PLZmi2RVNSjik2NtaHrzQ0WdbVwGPj8vwy6eMDElPWvSkl/q8wZ/dEV+Hhllj2dLb7v2UJV4Lya49sLDJXklh294OIPcnm/I6fOpFbzvw3Uurc/LtzeVTuBKla46zs3VnQ7/sL37B80NNFt6H0OOU6uWbk0+JoTilUqFC662hrgmvgoTSDUqRIEbn11ltl1qxZYukPd3bOfGgtxsSJE01GwlGzceedd7r91V4xo0aNkmeeecYUkTqaTrSWY/r06Sb1lBZdt3Llyua2bkPTT99//73cc889qa4fGRlpJvi2qWXN4oIyevYhicprN2N6qDz5kiQyypIyleOkZIV4ee35MtJt5AmJLphoAhUd82PsvKtdE7VXy74duaXmrZckb4FEOXkk0jTVlCgfL9XrX+LjQpby/bcx8q8nfpHfT0eZZpdKN5yTB/51UFYsczS52GTJx5Xk4U6/yInYPHLqZB55/Kk9cuaPXLLpmxIB3ntkhavalilztT7IQY9fo0ePTvtxdrv069fP1FBqxiM1//3vf2XcuHHSvXt3t/l6wn/XXXeZMoWvv/7aHGs1IaD1Idky+NA3SzMdDz/8sLRo0SLddQcOHGiaXfTvu+++a+ZNmzZN2rdvb3rMpEWbZU6dOmWiRm3G0WjPEYzAP5bOLWL+DmpfxW3+gCnHTBdc7a3y4r8PyszxJc1AYn9dCpOSFa7IwNeOya13/2nWjYyyy7f/yS//fiXGjA9SqFiC3NzsTxnW96hERAbpeMTItnR8j8e67ZVnBvwk+QvGm1qP/3xeXj6YXdW5zifzK0uuXInS5/kfJU/eBNn9cyEZMaChJFy52hsMoS02NtZkKByudVKsmQs9md+wYUOqyzV70rp1a1P7kTyI0c4eDno8vXTpkmlVyLbBx2uvvSZHjhyRZcuWXXNdDRo0FaQpoUcffdQZvGiTTXo0UNEpIiJCqlSpIvPnzzfddOE/X53Ycc11SlW8IiPfO5Lmci06nfTxQR/vGZA5/vorp7z7ei0zpc0m78+sbiZkD3YfjnCqgYdr8JEerYlcunSprF+/XkqXvlpn5OrPP/80xaTaK2bx4sWSM2f64xM0aNDAZEj0pN3TloCgCj769+9vpuS0aSW19iYdp0OLU5UGLclpsU3ykVIBAAi2ZhdP6HFSe4hqQKHHuwoVKqSa8dCWBQ0itLwhV66/C5zTonWSBQsWzFAJQlAFHwAA4PpoU8uCBQtMZwzNamiJgaPWUcf10MBDu95q+cH777/vLF5VRYsWlfDwcNOZ4/Tp03LbbbeZwETH1Ro/frwpccgIgg8AAALA7oNrs2Tk8drhwtFakHzMDu3duX37dtm8ebOZl7zW8fDhw6YEQZtgtH5SWyE0k6Lrvfrqq84hLzxF8AEAQIg0u6QnrRIGV1oL4jq42PViwAMAAOBXZD4AAAiBzEdWQvABAEAA2EM4+KDZBQAA+BWZDwAAAsAewpkPgg8AAALAymBX2bS2EYwIPgAACAB7CGc+qPkAAAB+ReYDAIAAsIdw5oPgAwCAALCHcPBBswsAAPArMh8AAASAPYQzHwQfAAAEgGXZzOTtNoIRzS4AAMCvyHwAABAAdrF5PciYt48PFIIPAAACwB7CNR80uwAAAL8i8wEAQABYIVxwSvABAEAA2EO42YXgAwCAALBCOPNBzQcAAPArMh8AAASA5YNml2DNfBB8AAAQAJYJHrzfRjCi2QUAAPgVmQ8AAALALjbzn7fbCEYEHwAABIBFbxcAAAD/IPMBAEAA2C2b2BhkDAAA+Itl+aC3S5B2d6G3CwAA8CuCDwAAAlhwank5eWrChAlyyy23SL58+aRYsWLSrl072bdvn9s6cXFx0qtXLylcuLDkzZtX2rdvL6dPn3Zb59ixY9K6dWvJnTu32c6gQYMkMTExQ6+d4AMAgBAIPtatW2cCi++++05WrFghCQkJ0rx5c7l06ZJznf79+8sXX3whH3/8sVn/xIkT8uCDDzqXJyUlmcDjypUrsnHjRpk7d67MmTNHRo4cmaHXTsEpAAAhUHC6fPlyt/saNGjmYtu2bdK4cWM5f/68zJw5UxYsWCB33XWXWWf27NlSvXp1E7Dcdttt8vXXX8vu3btl5cqVUrx4calbt66MGzdOBg8eLKNHj5aIiAiP9oXMBwAAQe7ChQtuU3x8/DUfo8GGKlSokPmrQYhmQ+655x7nOtWqVZOyZcvKpk2bzH39W6tWLRN4OLRo0cI8565duzzeX4IPAAAC2NvF8nJSZcqUkfz58zsnre9Ij91ul379+kmjRo2kZs2aZt6pU6dM5qJAgQJu62qgocsc67gGHo7ljmWeotkFAIAAsEzw4O1Vba/+jY2NlejoaOf8yMjIdB+ntR87d+6UDRs2SCCQ+QAAIMhFR0e7TekFH71795alS5fKmjVrpHTp0s75MTExppD03Llzbutrbxdd5lgnee8Xx33HOp4g+AAAIAR6u1iWZQKPxYsXy+rVq6VChQpuy+vXry85c+aUVatWOedpV1ztWtuwYUNzX//+/PPP8ttvvznX0Z4zGvDUqFHD432h2QUAgACw/jd5uw1PaVOL9mRZsmSJGevDUaOhNSJRUVHmb9euXeW5554zRagaUPTp08cEHNrTRWnXXA0yHn/8cZk0aZLZxvDhw822r9XU44rgAwCAEDB9+nTzt2nTpm7ztTtt586dze0pU6ZIWFiYGVxMe8xoT5a33nrLuW54eLhpsunZs6cJSvLkySOdOnWSsWPHZmhfCD4AAAgAK4PNJmltw/N1r50nyZUrl0ybNs1MaSlXrpwsW7ZMvEHwAQBAKLS7ZCEEHwAABILlfeZDtxGM6O0CAAD8iswHAAABYLmMUOrNNoIRwQcAACFQcJqV0OwCAAD8iswHAACBYNm8LxgN0swHwQcAAAFghXDNB80uAADAr8h8AAAQCBaDjKXr888/9/i9vP/++73/QAAAyOasEO7t4lHmo127dh5tzGazSVJSkrf7BAAAsjGPgg+73Z75ewIAQKixJCR5VfMRFxdnroAHAAAyxgrhZpcM93bRZpVx48ZJqVKlJG/evHLo0CEzf8SIETJz5szM2EcAALJvwanl5RQKwcdLL70kc+bMkUmTJklERIRzfs2aNeW9997z9f4BAIBsJsPBx7x58+Sdd96Rjh07Snh4uHN+nTp1ZO/evb7ePwAAsimbj6YQqPn49ddfpXLlyqkWpSYkJPhqvwAAyN6s0B3nI8OZjxo1asg333yTYv4nn3wi9erV89V+AQCAbCrDmY+RI0dKp06dTAZEsx2LFi2Sffv2meaYpUuXZs5eAgCQ3VhkPjzWtm1b+eKLL2TlypWSJ08eE4zs2bPHzLv33nsz82MCACD7XdXW8nIKlXE+7rzzTlmxYoXv9wYAAGR71z3I2NatW03Gw1EHUr9+fV/uFwAA2ZplXZ283UZIBB/Hjx+XRx55RL799lspUKCAmXfu3Dm5/fbbZeHChVK6dOnM2E8AALIXi5oPjz311FOmS61mPc6cOWMmva3Fp7oMAADAp5mPdevWycaNG6Vq1arOeXr7jTfeMLUgAADAA5YPCkZDpeC0TJkyqQ4mptd8KVmypK/2CwCAbM1mXZ283UZIDDI2efJk6dOnjyk4ddDbffv2lZdfftnX+wcAQPZkhe6F5TzKfBQsWFBstr9TO5cuXZIGDRpIjhxXH56YmGhud+nSRdq1a5d5ewsAAIKeR8HH1KlTM39PAAAIJRY1H+nS4dQBAEBwd7Vdv369KZ/Ytm2bnDx5UhYvXuzWYuHayuFq0qRJMmjQIHO7fPnycvToUbflEyZMkCFDhmT+IGMqLi5Orly54jYvOjram00CAIBMomUTderUMWUSDz74YIrlGpC4+s9//iNdu3aV9u3bu80fO3asdOvWzXk/X758GdqPHNez44MHD5aPPvpI/vjjj1R7vQAAgKyX+WjVqpWZ0hITE+N2f8mSJdKsWTOpWLGi23wNNpKvm6m9XZ5//nlZvXq1TJ8+XSIjI+W9996TMWPGmG62emVbAAAQ/L1dTp8+LV9++aXJfCQ3ceJEKVy4sNSrV88042jHk0zNfOjVazXIaNq0qTz55JNmYLHKlStLuXLlZP78+dKxY8eMbhIAAHjhwoULbvc1OaCTN+bOnWsyHMmbZ5599lm56aabpFChQmbQ0aFDh5rmmldffTXzgg8dTt2RftH6Dr2v7rjjDunZs2dGNwcAQGiyfNfbRQcAdTVq1CgZPXq0V5ueNWuWSSjkypXLbf5zzz3nvF27dm2JiIiQp59+2hSdehrwZDj40MDj8OHDUrZsWalWrZqp/bj11ltNRsRxoTkAAOC/EU5jY2PdOnx4m/X45ptvZN++ffLhhx9ec10d90ubXY4cOeJ26RWfBh/a1PLjjz9KkyZNTLeaNm3ayJtvvmmGXM9IygUAAPiGBh6+7G06c+ZMqV+/vukZcy07duyQsLAwKVasmMfbz3Dw0b9/f+fte+65R/bu3Wv6C2vdh6ZfAABA1uztcvHiRTlw4IDzvrZkaPCg9RvaouGoH/n444/llVdeSfH4TZs2yebNm00PGK0H0fsaFzz22GNmNHS/jPOhtNBUJwAAkLVt3brVBA7J6zd0MNE5c+aY2wsXLhTLsuSRRx5J8XhtztHlWk8SHx8vFSpUMMGHax2Iz4KP119/3eMNahUsAABIn80HV6XNaLmq9lTVwCI93bt3N1NqtJfLd999J97yKPiYMmWKRxvTYVkJPgAAgNfBh7YJIWMeuKGW5LDl5G1DthSWL+XoxkB2YLfcLxmSqSwuLAcAALJ5wWlWkeHh1QEAALzhdW8XAABwHazQzXwQfAAAEOQjnAYbml0AAEDWDz50zHcdzaxhw4by66+/mnn//ve/ZcOGDb7ePwAAsnezi+XlFArBx6effiotWrSQqKgo+eGHH8wIZ+r8+fMyfvz4zNhHAACyH4vgw2MvvviizJgxQ959913JmfPvcSwaNWok27dvz5wPCAAAZBsZLjjVS+w2btw4xfz8+fPLuXPnfLVfAABkazYKTj0XExPjdkU8B633qFixok8/GAAAsi3L5pspFGo+unXrJn379jWX1NVruZw4cULmz58vAwcOlJ49e2bOXgIAkN1YoVvzkeFmlyFDhojdbpe7775bLl++bJpg9BK7Gnz06dMnc/YSAABkGxkOPjTbMWzYMBk0aJBpfrl48aLUqFFD8ubNmzl7CABANmQL4ZqP6x7hNCIiwgQdAADgOlgMr+6xZs2amexHWlavXs13EAAA+C7zUbduXbf7CQkJsmPHDtm5c6d06tQpo5sDACA0WT5oNgmVZpcpU6akOn/06NGm/gMAAHjACt1mF59dWE6v9TJr1ixfbQ4AAGRT111wmtymTZskV65cvtocAADZmxW6mY8MBx8PPvig233LsuTkyZOydetWGTFihC/3DQCAbMtGV1vP6TVcXIWFhUnVqlVl7Nix0rx5c59/OAAAIHvJUOYjKSlJnnzySalVq5YULFgw8/YKAABkWxkqOA0PDzfZDa5eCwCAl6zQvbZLhnu71KxZUw4dOpQ5ewMAQIjVfNi8nEIi+HjxxRfNReSWLl1qCk0vXLjgNgEAAPik5kMLSgcMGCD33XefuX///fe7DbOuvV70vtaFAAAAD1ih+S55HHyMGTNGevToIWvWrMncPQIAIBRYjPNx7ffIuhqeNWnSJPM/EAAAkG1lqKttelezBQAAGTimWt4XjIZEwekNN9wghQoVSncCAABZs6vt+vXrpU2bNlKyZEmTUPjss8/clnfu3NnMd51atmzpts6ZM2ekY8eOEh0dLQUKFJCuXbtm+MKyGcp8aN1H8hFOAQBAcLh06ZLUqVNHunTpkuJyKQ4abMyePdt5PzIy0m25Bh7a23XFihWSkJBgBh/t3r27LFiwIHOCj4cffliKFSuWkYcAAIAs0uzSqlUrM6VHg42YmJhUl+3Zs0eWL18uW7ZskZtvvtnMe+ONN0xP2JdfftlkVHza7EK9BwAAWbPZ5UKyMbfi4+Ove7fWrl1rEg163baePXvKH3/84XYFe21qcQQe6p577jHXedu8ebPHzxGW0d4uAAAgaylTpowpi3BMEyZMuK7taJPLvHnzZNWqVfJ///d/sm7dOpMpcYzhderUqRQtIDly5DA1n7rM580udrs9I/sPAAD8NM5HbGysKQBNq04jI+UVDnoR2dq1a0ulSpVMNuTuu++WgA2vDgAAsta1XaKjo92m6w0+kqtYsaIUKVJEDhw4YO5rLchvv/3mtk5iYqLpAZNWnUhqCD4AAAgEK+tf1fb48eOm5qNEiRLmfsOGDc2V7bdt2+ZcZ/Xq1aZ1pEGDBpnT2wUAAASvixcvOrMY6vDhw7Jjxw7nWF06pEb79u1NFuPgwYPy/PPPS+XKlaVFixZm/erVq5u6kG7dusmMGTNMV9vevXub5hpPe7ooMh8AAIRI5mPr1q1Sr149M6nnnnvO3B45cqSEh4fLTz/9ZC4cq4OK6uBh9evXl2+++catGWf+/PlSrVo1UwOiXWzvuOMOeeeddzK0H2Q+AAAIkXE+mjZtmm7v1a+++uqa29AMSUYGFEsNmQ8AAOBXZD4AAAjyrrbBhuADAIAAsHFVWwAAAP8g8wEAQCBYNLsAAACCD7+gtwsAAPArml0AAAgA2/8mb7cRjAg+AAAIBIuaDwAA4Ec2utoCAAD4B80uAAAEgkWzCwAACEQAEoLoagsAAPyKZhcAAALAFsIFpwQfAAAEghW6NR80uwAAAL8i8wEAQADYaHYBAAB+ZdHsAgAA4Bc0uwAAEAA2ml0AAIBfWaHb7ELmAwCAQLBCN/igqy0AAPArMh8AAASAjZoPAADgVxbNLgAAAH5BswsAAAFgsywzebuNYETwAQBAIFg0uwAAAPgFXW0BAAhgbxebl1NGrF+/Xtq0aSMlS5YUm80mn332mXNZQkKCDB48WGrVqiV58uQx6zzxxBNy4sQJt22UL1/ePNZ1mjhxYob2g+ADAIBANrtYXk4ZcOnSJalTp45MmzYtxbLLly/L9u3bZcSIEebvokWLZN++fXL//fenWHfs2LFy8uRJ59SnT58M7Qc1HwAAhIhWrVqZKTX58+eXFStWuM1788035dZbb5Vjx45J2bJlnfPz5csnMTEx170fZD4AAAjyZpcLFy64TfHx8T7Zx/Pnz5tmlQIFCrjN12aWwoULS7169WTy5MmSmJiYoe2S+QAAIMh7u5QpU8Zt9qhRo2T06NFebTouLs7UgDzyyCMSHR3tnP/ss8/KTTfdJIUKFZKNGzfK0KFDTdPLq6++6vG2CT4AAAjy4dVjY2PdAoTIyEivtqvFpw899JBYliXTp093W/bcc885b9euXVsiIiLk6aeflgkTJnj8vDS7AAAQ5KKjo90mb4IPR+Bx9OhRUwPiGtSkpkGDBqbZ5ciRIx4/B5kPAAACwcp6g4w5Ao/9+/fLmjVrTF3HtezYsUPCwsKkWLFiHj8PwQcAAAFi8/Po6BcvXpQDBw447x8+fNgED1q/UaJECfnnP/9putkuXbpUkpKS5NSpU2Y9Xa7NK5s2bZLNmzdLs2bNTI8Xvd+/f3957LHHpGDBgh7vB8EHAAAhYuvWrSZwSF6/0alTJ1Og+vnnn5v7devWdXucZkGaNm1qmnMWLlxo1tUeNRUqVDDBh2sdiCcIPgAACATLujp5u40M0ABCi0jT3lz629NeLt999514i+ADAIAg7+0SbOjtAgAA/IrMBwAAgWBlvd4u/kLwAQBAANjsVydvtxGMaHYBAAB+FVSZj/Hjx5u+xzqW/I033ii9evWSZ555JtC7hUz22IBT8viA027zYg9EylONq5nbBYsmyFMjTspNjf+U3HntEnswUha+Vkw2LHO/EBKQlRUuFi9dBh2Rm+88K5FRdjlxNJdMeaGK7N+Zz7lOmYqXzTq1bjkv4eGWHDuYW17sU01+P5kroPuO62TR7BIQnTt3lrlz55ox4WfMmOG2TAOLt956y/Q9njNnjpnXo0cPefzxx83tZcuWpbjKHrKvI3tzyZB/VXTeT0qyOW8Pev2Y5I1OktGdK8j5M+HS7IFz8sLbR6VPqwg5uDN3gPYY8Fze6ER55YOf5MfN+WVEtxvl/NmcUqrcX3Lx/N/nhyXK/CUvL/hJvvq0uLz/elm5fDFcyla5LFfiSWAHK1sI93YJeOZDr8SnA5ZMmTJFoqKinFfSW7BggZQtW9ZtXR1hTSdVrly5gOwvAiMpSeTs7zlTXVbj5svyxpBSsm/H1UDjg9eKy4Pdfpcqtf8i+EBQ6NDtuPx+KlKmvHCDc97p4+7ZjE79j8qW9QVl1uQKznknY6/+ZiJIWf4f5yOrCHjIrAOWaACyaNEi5zy9rYFHvXr1nPPsdru5Yp6OpqZBSp06deSTTz5xLl+7dq3YbDb58ssvzVX2cuXKJbfddpvs3LnTbXlak9IMi+N+eHi4lCxZ0lxOWJ8bgVWqwhVZsH2XzNm0Rwa/eVSKlrriXLZ7a25pcv85yVcgUWw2S5q0PSsRuSz5aWPegO4z4Knb7vpD9u/MKy+8tkc+2LhZ3lz8g7TscHVYa6Xf61uanpVfj0TJi+/tNOtM+WiHNLz7D95kBKWABx+qS5cuMnv2bOf9WbNmyZNPPum2jgYe8+bNM80zu3btco4lv27dOrf1Bg0aJK+88ops2bJFihYtKm3atDEXyrn99tvl5MmTZvr000/Nuo77Ojno1fv0/rFjx0w2ZtKkSfLVV1+lue86vOyFCxfcJvjW3u255eV+ZWRYx4omwxFT9oq8sviAROVJMstferq8hOe05JPdu2TpkZ+k7/8dlzFdy8uJI95dUhrwl5gycdL6kZMmuBje9Ub58oMS0mP4Ibmn3dVapwKFEyR3niR5qNtx2fpNQRnW5UbZuKKwDH9zj6n/QHA3u9i8nIJRwJtdlAYRQ4cONZfvVd9++61pitFsheMAr8WmK1eulIYNG5p5FStWlA0bNsjbb78tTZo0cW5r1KhRcu+995rbWk9SunRpWbx4sblKX0xMjJnvaLpx3HelWQ/HfM2y6JX68ufPn+a+a1A0ZswYH74bSG7rmr8v53x4T5Ts/SGP/Pv73dL4/nPy1QeFpdPzJyVvtF0GP1RRLpzJIQ1bnpdhM47IgAcqy5G9pKWR9WnyVTMfc6eUN/cP7skr5apckvsePiUrPysutrCrR5hNqwrLZ3NLmduH9uaVGjf9Kfc9fFJ+3pL2bxSyMIuC04DSDEXr1q1Ns4eOK6+3ixQp4lyuV+C7fPmyM6hwuHLlilvTjHIEJ44go2rVqrJnzx6P9+X8+fOSN29eczU/DXqGDBlisiZp0aDJ9YI6mvnQZiRknksXwuX4oUgpWf6KlCgXL227/CHdm1aVo79cbSM/tDtKajW4JPd3/kNeH1KajwJZ3pnfI0zPFVexh3JLoxZXm1UunM0piQk2OXbQPZiOPRglNeqTbUXwyRKZD0fTS+/evc3tadOmpbgEsNJ6jlKlrkb9DnqFPV/SSwTr5YQ1CNLmHd2v+vXrS/v27VNdX5/f1/uA9OXKnSQly12RVZ/mMF0SVfKyHC1QdZwtAlnd7u3RUrrCX27zSpX/S3779epvS2JCmPzyc9401qGbbbCyhXBvlyxR86FatmxpMhlan9GiRQu3ZTVq1DAHeK3DqFy5stuUPMvgerW9s2fPyi+//CLVq1f3eD+0mUW3W6VKFWnXrp3cddddptkGgdNt5AmpddtFKV76itS4+ZKMmnVEkuwiaxcXlNgDueTXQxHSd9JxqVr3ssmEtH/6N7mp8UXZuJxUNILDZ3NLSrU6f8q/no6VEmX/kqb/+E1aPXRKli4o4Vzn05mlpHGr/5pCVF2nTccT0qDZGfnyg5TNxwiy3i6Wl1MQyjKZD+1d4mge0dvJsxEDBw40Raba8+SOO+4wzSNaG6IFojoWiMPYsWOlcOHCUrx4cRk2bJhpvtEgwlOa8Th16pT5u3fvXlPQ2rdvXx++UmRUkRIJMvSto5KvYJKc/yOH7NqSR/r9o4qcP3P16zv88YrS9YWTMmbuYYnKY5cThyPk5b5lZMvqv2tFgKzsl5/zybje1aXzc0fk0V7H5NTxXPL2+Iqy5otiznU2riwib45OlIe6HzfFqMcPR8mLz1aXXdsIshF8skzwoTSQSMu4ceNMbYgWeB46dMgMMKbddF944QW39SZOnGiChf3790vdunXliy++kIiICI/3QWs2SpQoYQpPNYDp0KGD6UGDwJnQM/0xXU4cjpRx3a4W6gHB6vu1hcyUnq8/jTETsgdbCDe72Cw9xc8GtGdMs2bNTFNLIEc+1eBFe8c0lbaSw5b6oFhAsAvL9/eQ30B2kmhdkdV/zjfZ9fROiH1xnGjYcqzkyOldzU5iQpxsWj4yU/c3W9d8AACA0JClml0AAAgVthBudsk2wUfTpk1NkSgAAEHBbl2dvN1GEMo2wQcAAEHFCt0RTqn5AAAAfkXmAwCAALD5oGbj6jXZgw/BBwAAgWD5YITSIK11pNkFAAD4FZkPAAACwEZXWwAA4FcWvV0AAAD8gmYXAAACwGZZZvJ2G8GI4AMAgECw/2/ydhtBiN4uAACEiPXr10ubNm2kZMmSYrPZ5LPPPnNbrpcpGTlypJQoUUKioqLknnvukf3797utc+bMGenYsaO5iq5eRb5r165y8eLFDO0HwQcAAAFsdrF5OWXEpUuXpE6dOjJt2rRUl0+aNElef/11mTFjhmzevFny5MkjLVq0kLi4OOc6Gnjs2rVLVqxYIUuXLjUBTffu3TO0HzS7AAAQIr1dWrVqZaZUN2VZMnXqVBk+fLi0bdvWzJs3b54UL17cZEgefvhh2bNnjyxfvly2bNkiN998s1nnjTfekPvuu09efvllk1HxBJkPAAACOcKp5eUkIhcuXHCb4uPjM7w7hw8fllOnTpmmFof8+fNLgwYNZNOmTea+/tWmFkfgoXT9sLAwkynxFMEHAABBrkyZMiZQcEwTJkzI8DY08FCa6XCl9x3L9G+xYsXclufIkUMKFSrkXMcTNLsAABDkI5zGxsaaAlCHyMhIycrIfAAAEOTNLtHR0W7T9QQfMTEx5u/p06fd5ut9xzL9+9tvv7ktT0xMND1gHOt4guADAABIhQoVTACxatUq57uh9SNay9GwYUNzX/+eO3dOtm3b5lxn9erVYrfbTW2Ip2h2AQAgAGz2q5O328gIHY/jwIEDbkWmO3bsMDUbZcuWlX79+smLL74oVapUMcHIiBEjTA+Wdu3amfWrV68uLVu2lG7dupnuuAkJCdK7d2/TE8bTni6K4AMAgECw/m428WobGbB161Zp1qyZ8/5zzz1n/nbq1EnmzJkjzz//vBkLRMft0AzHHXfcYbrW5sqVy/mY+fPnm4Dj7rvvNr1c2rdvb8YGyQibpR174TOaotJK46bSVnLYcvLOIlsKy5cv0LsAZIpE64qs/nO+nD9/3q2AM1OOE7cOkxw5/j6oX4/ExDhZ+/1Lmbq/mYHMBwAAITLIWFZB8AEAQADYQviqtvR2AQAAfkXmAwCAECk4zSoIPgAACARLRLzsakvNBwAA8JiNmg8AAAD/oNkFAICAdbW1vN9GECL4AAAgEKzQLTilqy0AAPArMh8AAASCXatOfbCNIETwAQBAANjo7QIAAOAfZD4AAAgEK3QLTgk+AAAIBCt0gw96uwAAAL8i8wEAQCBYoZv5IPgAACAQ7HS1BQAAfmSjqy0AAIB/0OwCAEAgWNR8AAAAf7Jb2vbi/TaCEF1tAQCAX9HsAgBAIFg0uwAAAP9GH+L9OB00uwAAAFwTzS4AAASCRbMLAADwJ7s2mdDbBQAAINPR7AIAQCBY9quTt9sIQozzAQBAIGs+LC+nDChfvrzYbLYUU69evczypk2bpljWo0cPn790Mh8AAIRIzceWLVskKSnJeX/nzp1y7733SocOHZzzunXrJmPHjnXez507t/gawQcAACGiaNGibvcnTpwolSpVkiZNmrgFGzExMZm6HzS7AAAQ5M0uFy5ccJvi4+Ov+fRXrlyR999/X7p06WKaVxzmz58vRYoUkZo1a8rQoUPl8uXLPn/pZD4AAAgE638BiLfbEJEyZcq4zR41apSMHj063Yd+9tlncu7cOencubNz3qOPPirlypWTkiVLyk8//SSDBw+Wffv2yaJFi8SXCD4AAAhysbGxEh0d7bwfGRl5zcfMnDlTWrVqZQINh+7duztv16pVS0qUKCF33323HDx40DTP+ArBBwAAgeDDEU418HANPq7l6NGjsnLlymtmNBo0aGD+HjhwgOADAICgZ9cxOuw+2EbGzZ49W4oVKyatW7dOd70dO3aYv5oB8SUyHwAAhBC73W6Cj06dOkmOHH+HAdq0smDBArnvvvukcOHCpuajf//+0rhxY6ldu7ZP94HgAwCAELqw3MqVK+XYsWOml4uriIgIs2zq1Kly6dIlU8Tavn17GT58uPgawQcAACEUfDRv3lysVB6nwca6devEHxjnAwAA+BWZDwAAQmR49ayC4AMAgACwLLuZvN1GMCL4AAAgECzL+8yFtzUjAULNBwAA8CsyHwAABILlg5qPIM18EHwAABAIdruIzcuajSCt+aDZBQAA+BWZDwAAAsGi2QUAAPgz9rDbxbKFZldbml0AAIBf0ewCAEAgWDS7AAAAf7JbIrbQ7GpLswsAAPArml0AAAgES7MW9pDMfBB8AAAQAJbdEsvLZheL4AMAAGQg+hDvMx90tQUAALgmml0AAAgAi2YXAADg5+hDQrXZhcyHjzmKfxIlwesrJQNZVZh1JdC7AGSKRCvBb4WciT44TphtBCGCDx/7888/zd8NsszXmwayjqtfcyBb/5bnz58/U7YdEREhMTExsuGUb44Tui3dZjCxWcHaTyeLstvtcuLECcmXL5/YbLZA7062d+HCBSlTpozExsZKdHR0oHcH8Dm+4/6lh0QNPEqWLClhYZk3DmdcXJxcueKbDKIGHrly5ZJgQubDx/TLWrp0aV9vFteggQfBB7IzvuP+k1kZD1e5cuUKuoDBlxheHQAA+BXBBwAA8CuCDwS1yMhIGTVqlPkLZEd8x5EdUXAKAAD8iswHAADwK4IPAADgVwQfAADArwg+ACCAxo8fL7fffru5feONN8pbb73F54Fsj+ADAffLL79I8eLFpWfPnm7z165da0aJPXfunNuogM2aNZN69eq5zQeyis6dO5vvbY8ePVIs69Wrl1mm6zjoeh9++KG5vWzZMunYsaNf9xcIBIIPBNwNN9wgy5cvlw8++ECGDh2a5nqJiYnSoUMHM3z9V199JQUKFPDrfgKe0iH/Fy5cKH/99Zdb4LxgwQIpW7as27qFChUy66ty5cr5ZXRNINAIPpAlaCbjyy+/lNdff10mTZqU6jVzOnXqJD/++KOsWLFCihUrZuaXL19epk6d6lxv1apV5syyXbt2znlNmzY183SKioqSunXrmmAHyCw33XSTCSgWLVrknKe3NfDQ77rr93rChAlSoUIF892sU6eOfPLJJymyf/pvo3bt2mY47ttuu0127tzptjytSc2ZM8d5Pzw83FyzZPDgwea5gUAh+ECW0ahRI/MDPWLECHn77bdTpKs16Fi5cmWKM0cH/TEdMGCA5M2bN8Wybt26ycmTJ82Pds2aNU0gA2SmLl26yOzZs533Z82aJU8++aTbOhp4zJs3T2bMmCG7du2S/v37y2OPPSbr1q1zW2/QoEHyyiuvyJYtW6Ro0aLSpk0bSUhIMLUi+r3W6dNPPzXrOu7r5HpdGL1/7NgxmTJlignwNXsIBAoXlkOWoilnDSL69OljfryVNsW899575gc0vaaWuXPnSnx8vLRt21YuXrzotix37tzmstPadKNZE1LbyGwaROh39+jRo+b+t99+a5piNFuh9LuqxaYaUDds2NDMq1ixomzYsMEE302aNHFuS0fxvffee53fc7145eLFi+Whhx4y32tH841y3HelWQ/HfM2y6AUw+TeAQCL4QJahl5d+6qmnTAGepp81W+H4sdXmlOHDh0vv3r3lo48+SvHYy5cvm+V6Buk4A3SlPQg0gNEffA1gPv/8c7+8JoQuzVC0bt3aNHvoZdr1dpEiRZzLDxw4YL63jqDC9d+Ba9OMcgQnjiCjatWqsmfPHo/35fz58yYjmJSUZP4NDBkyxNnDBggEgg9kGS+99JLJWGgqWn8oNU08cuRI0xOgcePGJnjQoGTJkiUmu+Fq8uTJ5gdZ09GpBR/ag2DYsGGm6E+DGS1c3b17t8mmAJlFs3caMKtp06a5LXNk57Seo1SpUm7LfH2tonz58sn27dtNEKTNO7pf9evXl/bt2/v0eQBPUfOBLEFrMSZOnGjSzY6ajTvvvNPtr/aK0fTzM888Y87kHDRI0fZwndKiKebKlSubeg/dxq+//irff/99pr8uhLaWLVuaTIbWZ7Ro0cJtWY0aNUyQoXUY+t10nRy9Xxy+++475+2zZ8+a7unVq1f3eD+0mUW3W6VKFVOMfdddd5lmGyBQyHwg4LTGQ5tbHn744RQ/0MkNHDjQNLvo33fffdd5RqlncMlT1a40vX3q1CmTctbMR44cOcyPMZCZtHeJo3lEbyfPRuj3WItM9d/AHXfcYYJqrQ3RjJxrUfTYsWOlcOHCZjwczeBp841rj65r0YyHfv/17969e01Ba9++fX34SoGMIfhAwL322mty5MgRM8DStWjQoL0Gbr31Vnn00UfNPP3h1iab9GigolNERIQ5+5s/f77ppgtktvSa9saNG2dqQ7Sp8dChQ6YeSbvpvvDCC27raVZQg4X9+/ebruJffPGF+S576sKFC1KiRAlTeKoBjDY7ag8aIFBslobCAIAsR3vG6Ii+2tTCoHrITqj5AAAAfkXwAQAA/IpmFwAA4FdkPgAAgF8RfAAAAL8i+AAAAH5F8AEAAPyK4APIhjp37uw2AmbTpk2lX79+ARmnQge2OnfuXJrr6PLPPvvM422OHj3aDLTlDR3UTp93x44dXm0HwPUh+AD8GBDoAU8nHZ1Sh3fXYbMTExMz/bkXLVpkRtP0VcAAAN5geHXAzxcamz17trnGjA4n36tXL8mZM6cMHTo0xbp6QbKMDKGdHr0MOwBkFWQ+AD/Sq5jGxMRIuXLlpGfPnnLPPffI559/7tZUotepKVmypFStWtXMj42NlYceesgMr61BRNu2bU2zgUNSUpI899xzZrlefOz55583FxBzlbzZRYOfwYMHm6un6j5pFmbmzJlmuzqctypYsKDJgOh+Oa6ho9cgqVChgkRFRUmdOnXkk08+cXseDaj06sO6XLfjup+e0v3SbeTOnVsqVqwoI0aMMFeFTU6vgKz7r+vp++N6pWP13nvvmSu/5sqVS6pVqyZvvfVWhvcFQOYg+AACSA/SmuFwWLVqlezbt09WrFghS5cudV6KXa+A+s0335grnubNm9d5qXb1yiuvyJw5c8wF9zZs2CBnzpy55uXSn3jiCfnggw/k9ddfN1dd1QO5blcP5p9++qlZR/fj5MmT5sJ/SgOPefPmyYwZM2TXrl3maqyPPfaYuUKqI0h68MEHpU2bNqaWQq9UPGTIkAy/J/pa9fXs3r3bPLdeEHDKlClu6xw4cMBc3VgvsLZ8+XL54Ycf5JlnnnEu1wsHjhw50gRy+vrGjx9vghi9ojGALEAvLAcg83Xq1Mlq27atuW23260VK1ZYkZGR1sCBA53LixcvbsXHxzsf8+9//9uqWrWqWd9Bl0dFRVlfffWVuV+iRAlr0qRJzuUJCQlW6dKlnc+lmjRpYvXt29fc3rdvn6ZFzPOnZs2aNWb52bNnnfPi4uKs3LlzWxs3bnRbt2vXrtYjjzxibg8dOtSqUaOG2/LBgwen2FZyunzx4sVpLp88ebJVv3595/1Ro0ZZ4eHh1vHjx53z/vOf/1hhYWHWyZMnzf1KlSpZCxYscNvOuHHjrIYNG5rbhw8fNs/7ww8/pPm8ADIPNR+AH2k2QzMMmtHQZoxHH33U9N5wqFWrlludx48//mjO8jUb4CouLk4OHjxomho0O9GgQQPnshw5csjNN9+counFQbMS4eHh0qRJE4/3W/fh8uXLcu+997rN1+xLvXr1zG3NMLjuh2rYsKFk1IcffmgyMvr6Ll68aApyk1+WvmzZslKqVCm359H3U7M1+l7pY7t27SrdunVzrqPbyZ8/f4b3B4DvEXwAfqR1ENOnTzcBhtZ1aKDgKk+ePG739eBbv35904yQXNGiRa+7qSejdD/Ul19+6XbQV1oz4iubNm2Sjh07ypgxY0xzkwYLCxcuNE1LGd1Xba5JHgxp0AUg8Ag+AD/S4EKLOz110003mUxAsWLFUpz9O5QoUUI2b94sjRs3dp7hb9u2zTw2NZpd0SyB1mpowWtyjsyLFrI61KhRwwQZx44dSzNjosWdjuJZh++++04yYuPGjaYYd9iwYc55R48eTbGe7seJEydMAOd4nrCwMFOkW7x4cTP/0KFDJpABkPVQcApkYXrwLFKkiOnhogWnhw8fNuNwPPvss3L8+HGzTt++fWXixIlmoK69e/eawsv0xugoX768dOrUSbp06WIe49imFnAqPfhrLxdtIvr9999NJkGbMgYOHGiKTLVoU5s1tm/fLm+88YaziLNHjx6yf/9+GTRokGn+WLBggSkczYgqVaqYwEKzHfoc2vySWvGs9mDR16DNUvq+6PuhPV60J5HSzIkWyOrjf/nlF/n5559NF+dXX301Q/sDIHMQfABZmHYjXb9+valx0J4kml3QWgat+XBkQgYMGCCPP/64ORhr7YMGCg888EC629Wmn3/+858mUNFuqFobcenSJbNMm1X04K09VTSL0Lt3bzNfBynTHiN6UNf90B432gyjXW+V7qP2lNGARrvhaq8Y7WWSEffff78JcPQ5dRRTzYTocyan2SN9P+677z5p3ry51K5d260rrfa00a62GnBopkezNRoIOfYVQGDZtOo0wPsAAABCCJkPAADgVwQfAADArwg+AACAXxF8AAAAvyL4AAAAfkXwAQAA/IrgAwAA+BXBBwAA8CuCDwAA4FcEHwAAwK8IPgAAgF8RfAAAAPGn/wfSmjmQjzWqWwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "\n",
    "ConfusionMatrixDisplay.from_predictions(\n",
    "    y_test,\n",
    "    rf_pred,\n",
    "    display_labels=[\"Жив\", \"Мёртв\"]\n",
    ")\n",
    "\n",
    "plt.title(\"Матрица ошибок Random Forest\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "905fc6a1",
   "metadata": {},
   "source": [
    "Далее сравним модели по ROC-кривым."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4feceec3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import RocCurveDisplay\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "RocCurveDisplay.from_predictions(\n",
    "    y_test,\n",
    "    logreg_prob,\n",
    "    name=\"Logistic Regression\",\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "RocCurveDisplay.from_predictions(\n",
    "    y_test,\n",
    "    rf_prob,\n",
    "    name=\"Random Forest\",\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.title(\"ROC-кривая\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6fa1c37",
   "metadata": {},
   "source": [
    "ROC-кривая подтверждает небольшое преимущество случайного леса: его AUC выше, поэтому он в среднем лучше ранжирует персонажей по риску смерти. Теперь посмотрим, насколько сильно перекрываются вероятности для реально живых и умерших персонажей."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5785b642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "\n",
    "plt.hist(\n",
    "    rf_prob[y_test.values == 0],\n",
    "    bins=20,\n",
    "    alpha=0.6,\n",
    "    label=\"Живы\"\n",
    ")\n",
    "\n",
    "plt.hist(\n",
    "    rf_prob[y_test.values == 1],\n",
    "    bins=20,\n",
    "    alpha=0.6,\n",
    "    label=\"Мертвы\"\n",
    ")\n",
    "\n",
    "plt.xlabel(\"Предсказанная вероятность смерти\")\n",
    "plt.ylabel(\"Количество персонажей\")\n",
    "plt.title(\"Распределение предсказанных вероятностей\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "420ab5e8",
   "metadata": {},
   "source": [
    "У умерших персонажей вероятности в среднем выше, но распределения заметно пересекаются. Это объясняет одновременно умеренный ROC AUC и наличие как пропущенных смертей, так и ложных тревог. Свяжем прогнозы с именами, чтобы посмотреть на конкретные примеры."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7b4089fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    name  actual_death  predicted_death  predicted_prob\n",
      "684   Lancel V Lannister             0                1        1.000000\n",
      "251               Wallen             1                1        1.000000\n",
      "281   Aegon II Targaryen             1                1        1.000000\n",
      "282   Aegon IV Targaryen             1                1        1.000000\n",
      "1077                Hake             1                1        0.998667\n",
      "531              Gelmarr             1                1        0.996667\n",
      "289    Aenys I Targaryen             1                1        0.994074\n",
      "1648  Baelor I Targaryen             1                1        0.993333\n",
      "1479               Chett             1                1        0.980000\n",
      "1618     Aegon Blackfyre             1                1        0.976667\n",
      "831         Ormund Wylde             0                1        0.966667\n",
      "1590      Otto Hightower             1                1        0.960000\n",
      "25      Willow Witch-eye             1                1        0.949222\n",
      "1533            Kurleket             1                1        0.940000\n",
      "477     Duncan Targaryen             1                1        0.933333\n",
      "                                 name  actual_death  predicted_death  \\\n",
      "1138                      Jorah Stark             0                0   \n",
      "137                           S'vrone             0                0   \n",
      "1223                     Megga Tyrell             0                0   \n",
      "1006                   Edderion Stark             0                0   \n",
      "1218                      Mathis Frey             0                0   \n",
      "804                             Narbo             0                0   \n",
      "1343                      Ryella Frey             0                0   \n",
      "23                             Willum             0                0   \n",
      "242    Walda Frey (daughter of Edwyn)             0                0   \n",
      "816                             Ocley             0                0   \n",
      "518                           Gallard             0                0   \n",
      "14                        Will (Treb)             0                0   \n",
      "855                           Poetess             0                0   \n",
      "241   Walda Frey (daughter of Lothar)             0                0   \n",
      "583                           Harodon             0                0   \n",
      "\n",
      "      predicted_prob  \n",
      "1138        0.003333  \n",
      "137         0.003333  \n",
      "1223        0.003333  \n",
      "1006        0.003333  \n",
      "1218        0.000000  \n",
      "804         0.000000  \n",
      "1343        0.000000  \n",
      "23          0.000000  \n",
      "242         0.000000  \n",
      "816         0.000000  \n",
      "518         0.000000  \n",
      "14          0.000000  \n",
      "855         0.000000  \n",
      "241         0.000000  \n",
      "583         0.000000  \n"
     ]
    }
   ],
   "source": [
    "predictions = df.loc[\n",
    "    X_test.index,\n",
    "    [\"name\"]\n",
    "].copy()\n",
    "\n",
    "predictions['actual_death'] = y_test\n",
    "predictions['predicted_death'] = rf_pred\n",
    "predictions['predicted_prob'] = rf_prob\n",
    "\n",
    "print(predictions.sort_values('predicted_prob', ascending=False).head(15))\n",
    "print(predictions.sort_values('predicted_prob', ascending=False).tail(15))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78d3a914",
   "metadata": {},
   "source": [
    "Среди персонажей с максимальным риском большинство действительно умерли, а в нижней части списка находятся живые персонажи. При этом встречаются и уверенные ошибки — например, Ланселю V Ланнистеру модель присвоила вероятность смерти 1.0, хотя фактический класс равен 0. Следующий график компактно показывает персонажей с самым высоким прогнозируемым риском."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f1c58eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_risk = predictions.sort_values('predicted_prob', ascending=False).head(15).sort_values('predicted_prob', ascending=True)\n",
    "\n",
    "plt.figure(figsize=(9, 6))\n",
    "\n",
    "plt.barh(top_risk['name'], top_risk['predicted_prob'])\n",
    "plt.title(\"Топ-15 персонажей с наибольшей вероятностью смерти\")\n",
    "plt.xlabel(\"Предсказанная вероятность смерти\")\n",
    "plt.ylabel(\"Персонажи\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b308e75c",
   "metadata": {},
   "source": [
    "Чтобы понять, на какие данные чаще опирается случайный лес, сопоставим признаки после one-hot encoding с рассчитанными моделью значениями важности. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c61dd7d9",
   "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>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>num__popularity</td>\n",
       "      <td>0.235495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>num__age</td>\n",
       "      <td>0.078788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>num__male</td>\n",
       "      <td>0.036648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>num__numDeadRelations</td>\n",
       "      <td>0.027229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>num__isNoble</td>\n",
       "      <td>0.019680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>num__isMarried</td>\n",
       "      <td>0.019098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>cat__house_Night's Watch</td>\n",
       "      <td>0.019004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>cat__culture_Northmen</td>\n",
       "      <td>0.015390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>cat__culture_Valyrian</td>\n",
       "      <td>0.015194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>cat__title_Ser</td>\n",
       "      <td>0.015119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>cat__house_House Frey</td>\n",
       "      <td>0.013497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>cat__culture_Ironborn</td>\n",
       "      <td>0.012538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>cat__house_House Targaryen</td>\n",
       "      <td>0.011185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>cat__house_House Greyjoy</td>\n",
       "      <td>0.010847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>cat__house_House Stark</td>\n",
       "      <td>0.008368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>cat__house_House Tully</td>\n",
       "      <td>0.007329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>cat__culture_Braavosi</td>\n",
       "      <td>0.007228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>cat__house_House Bracken</td>\n",
       "      <td>0.006728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>cat__house_House Lannister</td>\n",
       "      <td>0.006711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>cat__house_House Tyrell</td>\n",
       "      <td>0.006562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        feature  importance\n",
       "5               num__popularity    0.235495\n",
       "3                      num__age    0.078788\n",
       "0                     num__male    0.036648\n",
       "4         num__numDeadRelations    0.027229\n",
       "2                  num__isNoble    0.019680\n",
       "1                num__isMarried    0.019098\n",
       "557    cat__house_Night's Watch    0.019004\n",
       "238       cat__culture_Northmen    0.015390\n",
       "259       cat__culture_Valyrian    0.015194\n",
       "178              cat__title_Ser    0.015119\n",
       "365       cat__house_House Frey    0.013497\n",
       "228       cat__culture_Ironborn    0.012538\n",
       "503  cat__house_House Targaryen    0.011185\n",
       "378    cat__house_House Greyjoy    0.010847\n",
       "491      cat__house_House Stark    0.008368\n",
       "512      cat__house_House Tully    0.007329\n",
       "217       cat__culture_Braavosi    0.007228\n",
       "309    cat__house_House Bracken    0.006728\n",
       "411  cat__house_House Lannister    0.006711\n",
       "514     cat__house_House Tyrell    0.006562"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names = (\n",
    "    rf\n",
    "    .named_steps[\"preprocessor\"]\n",
    "    .get_feature_names_out()\n",
    ")\n",
    "\n",
    "feature_importances = (\n",
    "    rf\n",
    "    .named_steps[\"model\"]\n",
    "    .feature_importances_\n",
    ")\n",
    "\n",
    "importance_df = pd.DataFrame({\n",
    "    \"feature\": feature_names,\n",
    "    \"importance\": feature_importances\n",
    "}).sort_values(\n",
    "    \"importance\",\n",
    "    ascending=False\n",
    ")\n",
    "\n",
    "importance_df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fd65c81",
   "metadata": {},
   "source": [
    "Самым важным признаком с большим отрывом стала популярность персонажа, далее идут возраст, пол и количество погибших родственников. Среди категорий выделяются принадлежность к Ночному Дозору и некоторые культуры и дома. Результат согласуется с EDA."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}