09_hotel/09_hotel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. <a id='toc1_'></a>[Прогнозирование оттока клиентов в сети отелей «Как в гостях»](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Описание проекта` - цель данного проекта: выявить потенциальных постояльцев отеля которые с большой долей вероятности отменят бронирование гостиничного номера. Данное исследование поможет улучшить планирование расходов бизнеса и минимизировать финансовые издержки для отеля."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Содержание**<a id='toc0_'></a>    \n",
    "- 1. [Прогнозирование оттока клиентов в сети отелей «Как в гостях»](#toc1_)    \n",
    "    - 1.1.1. [Шаг 1. Откройте файлы с данными](#toc1_1_1_)    \n",
    "    - 1.1.2. [Шаг 2. Предобработка и исследовательский анализ данных](#toc1_1_2_)    \n",
    "      - 1.1.2.1. [Предобработка данных](#toc1_1_2_1_)    \n",
    "    - 1.1.3. [Исследовательский анализ данных](#toc1_1_3_)    \n",
    "    - 1.1.4. [Шаг 3. Формулировка ML-задачи на основе бизнес-задачи](#toc1_1_4_)    \n",
    "    - 1.1.5. [Шаг 4. Разработка модели ML](#toc1_1_5_)    \n",
    "      - 1.1.5.1. [Применение лучшей модели на тестовой выборке](#toc1_1_5_1_)    \n",
    "    - 1.1.6. [Шаг 5. Опишите портрет «ненадёжного» клиента](#toc1_1_6_)    \n",
    "    - 1.1.7. [Шаг 6. Напишите общий вывод](#toc1_1_7_)    \n",
    "\n",
    "<!-- vscode-jupyter-toc-config\n",
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    "<!-- THIS CELL WILL BE REPLACED ON TOC UPDATE. DO NOT WRITE YOUR TEXT IN THIS CELL -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.1. <a id='toc1_1_1_'></a>[Шаг 1. Откройте файлы с данными](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Импортируем необходимые модули и библиотеки согласно `PEP-8`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# стандартные библиотеки\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "# сторонние библиотеки\n",
    "from sklearn import datasets\n",
    "from sklearn import svm\n",
    "from scipy.stats import uniform\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Блок с константами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# бюджет выделенный на разработку модели\n",
    "BUDGET = 400000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Загружаем данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    df_train = pd.read_csv('hotel_train.csv')\n",
    "    df_test = pd.read_csv('hotel_test.csv')\n",
    "except:\n",
    "    df_train = pd.read_csv('/datasets/hotel_train.csv')\n",
    "    df_test = pd.read_csv('/datasets/hotel_test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проводим предворительное ознакомление с данными"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выводим основную информацию для каждой таблицы.\n",
    "Для оптимизации кода создаём функцию `show_i()` принимающую как аргумент датафрейм и выводящую на экран необходимую информацию."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_i(data):\n",
    "\n",
    "    '''Выводим на экран следущую информацию:\n",
    "\n",
    "    - размер таблицы\n",
    "    - основную информацию\n",
    "    - первые 5 строк таблицы\n",
    "    - суммарная статистика по числовым параметрам\n",
    "    - суммарная статистика по нечисловым параметрам'''\n",
    "\n",
    "    display(data.shape)\n",
    "    display(data.info())\n",
    "    display(data.head())\n",
    "    display(data.describe())\n",
    "    display(data.describe(include=['O']))\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Применяем функцию для ознакомления с данными"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65229, 25)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 65229 entries, 0 to 65228\n",
      "Data columns (total 25 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   id                              65229 non-null  int64  \n",
      " 1   is_canceled                     65229 non-null  int64  \n",
      " 2   lead_time                       65229 non-null  float64\n",
      " 3   arrival_date_year               65229 non-null  int64  \n",
      " 4   arrival_date_month              65229 non-null  object \n",
      " 5   arrival_date_week_number        65229 non-null  int64  \n",
      " 6   arrival_date_day_of_month       65229 non-null  int64  \n",
      " 7   stays_in_weekend_nights         65229 non-null  int64  \n",
      " 8   stays_in_week_nights            65229 non-null  int64  \n",
      " 9   adults                          65229 non-null  float64\n",
      " 10  children                        65229 non-null  float64\n",
      " 11  babies                          65229 non-null  float64\n",
      " 12  meal                            65229 non-null  object \n",
      " 13  country                         65229 non-null  object \n",
      " 14  distribution_channel            65229 non-null  object \n",
      " 15  is_repeated_guest               65229 non-null  int64  \n",
      " 16  previous_cancellations          65229 non-null  int64  \n",
      " 17  previous_bookings_not_canceled  65229 non-null  int64  \n",
      " 18  reserved_room_type              65229 non-null  object \n",
      " 19  booking_changes                 65229 non-null  int64  \n",
      " 20  days_in_waiting_list            65229 non-null  int64  \n",
      " 21  customer_type                   65229 non-null  object \n",
      " 22  required_car_parking_spaces     65229 non-null  int64  \n",
      " 23  total_of_special_requests       65229 non-null  int64  \n",
      " 24  total_nights                    65229 non-null  int64  \n",
      "dtypes: float64(4), int64(15), object(6)\n",
      "memory usage: 12.4+ MB\n"
     ]
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     "data": {
      "text/plain": [
       "None"
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       "      <th>id</th>\n",
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       "      <th>reserved_room_type</th>\n",
       "      <th>booking_changes</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>2015</td>\n",
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       "      <td>27</td>\n",
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       "      <td>14.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
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       "      <td>2015</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
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       "<p>5 rows × 25 columns</p>\n",
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      "text/plain": [
       "   id  is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0   0            0        7.0               2015               July   \n",
       "1   1            0       14.0               2015               July   \n",
       "2   2            0        0.0               2015               July   \n",
       "3   3            0        9.0               2015               July   \n",
       "4   4            1       85.0               2015               July   \n",
       "\n",
       "   arrival_date_week_number  arrival_date_day_of_month  \\\n",
       "0                        27                          1   \n",
       "1                        27                          1   \n",
       "2                        27                          1   \n",
       "3                        27                          1   \n",
       "4                        27                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  ...  \\\n",
       "0                        0                     1     1.0  ...   \n",
       "1                        0                     2     2.0  ...   \n",
       "2                        0                     2     2.0  ...   \n",
       "3                        0                     2     2.0  ...   \n",
       "4                        0                     3     2.0  ...   \n",
       "\n",
       "   is_repeated_guest  previous_cancellations previous_bookings_not_canceled  \\\n",
       "0                  0                       0                              0   \n",
       "1                  0                       0                              0   \n",
       "2                  0                       0                              0   \n",
       "3                  0                       0                              0   \n",
       "4                  0                       0                              0   \n",
       "\n",
       "  reserved_room_type booking_changes  days_in_waiting_list  customer_type  \\\n",
       "0   A                              0                     0      Transient   \n",
       "1   A                              0                     0      Transient   \n",
       "2   C                              0                     0      Transient   \n",
       "3   C                              0                     0      Transient   \n",
       "4   A                              0                     0      Transient   \n",
       "\n",
       "   required_car_parking_spaces total_of_special_requests  total_nights  \n",
       "0                            0                         0             1  \n",
       "1                            0                         1             2  \n",
       "2                            0                         0             2  \n",
       "3                            0                         1             2  \n",
       "4                            0                         1             3  \n",
       "\n",
       "[5 rows x 25 columns]"
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       "      <th>count</th>\n",
       "      <td>65229.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>43544.069172</td>\n",
       "      <td>0.368609</td>\n",
       "      <td>96.337917</td>\n",
       "      <td>2015.719021</td>\n",
       "      <td>31.339772</td>\n",
       "      <td>15.847813</td>\n",
       "      <td>0.825185</td>\n",
       "      <td>2.224793</td>\n",
       "      <td>1.836576</td>\n",
       "      <td>0.074890</td>\n",
       "      <td>0.007236</td>\n",
       "      <td>0.027151</td>\n",
       "      <td>0.128915</td>\n",
       "      <td>0.107713</td>\n",
       "      <td>0.194269</td>\n",
       "      <td>3.334422</td>\n",
       "      <td>0.056647</td>\n",
       "      <td>0.512763</td>\n",
       "      <td>3.049978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25614.858971</td>\n",
       "      <td>0.482431</td>\n",
       "      <td>96.127545</td>\n",
       "      <td>0.449481</td>\n",
       "      <td>13.464024</td>\n",
       "      <td>8.748182</td>\n",
       "      <td>0.853477</td>\n",
       "      <td>1.354992</td>\n",
       "      <td>0.480245</td>\n",
       "      <td>0.334243</td>\n",
       "      <td>0.098790</td>\n",
       "      <td>0.162523</td>\n",
       "      <td>0.965326</td>\n",
       "      <td>1.326638</td>\n",
       "      <td>0.593838</td>\n",
       "      <td>18.239606</td>\n",
       "      <td>0.232094</td>\n",
       "      <td>0.752590</td>\n",
       "      <td>1.738108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19108.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40554.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>67414.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>84121.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>374.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>259.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id   is_canceled     lead_time  arrival_date_year  \\\n",
       "count  65229.000000  65229.000000  65229.000000       65229.000000   \n",
       "mean   43544.069172      0.368609     96.337917        2015.719021   \n",
       "std    25614.858971      0.482431     96.127545           0.449481   \n",
       "min        0.000000      0.000000      0.000000        2015.000000   \n",
       "25%    19108.000000      0.000000     17.000000        2015.000000   \n",
       "50%    40554.000000      0.000000     64.000000        2016.000000   \n",
       "75%    67414.000000      1.000000    151.000000        2016.000000   \n",
       "max    84121.000000      1.000000    374.000000        2016.000000   \n",
       "\n",
       "       arrival_date_week_number  arrival_date_day_of_month  \\\n",
       "count              65229.000000               65229.000000   \n",
       "mean                  31.339772                  15.847813   \n",
       "std                   13.464024                   8.748182   \n",
       "min                    1.000000                   1.000000   \n",
       "25%                   21.000000                   8.000000   \n",
       "50%                   34.000000                  16.000000   \n",
       "75%                   42.000000                  23.000000   \n",
       "max                   53.000000                  31.000000   \n",
       "\n",
       "       stays_in_weekend_nights  stays_in_week_nights        adults  \\\n",
       "count             65229.000000          65229.000000  65229.000000   \n",
       "mean                  0.825185              2.224793      1.836576   \n",
       "std                   0.853477              1.354992      0.480245   \n",
       "min                   0.000000              0.000000      0.000000   \n",
       "25%                   0.000000              1.000000      2.000000   \n",
       "50%                   1.000000              2.000000      2.000000   \n",
       "75%                   2.000000              3.000000      2.000000   \n",
       "max                   4.000000              6.000000      4.000000   \n",
       "\n",
       "           children        babies  is_repeated_guest  previous_cancellations  \\\n",
       "count  65229.000000  65229.000000       65229.000000            65229.000000   \n",
       "mean       0.074890      0.007236           0.027151                0.128915   \n",
       "std        0.334243      0.098790           0.162523                0.965326   \n",
       "min        0.000000      0.000000           0.000000                0.000000   \n",
       "25%        0.000000      0.000000           0.000000                0.000000   \n",
       "50%        0.000000      0.000000           0.000000                0.000000   \n",
       "75%        0.000000      0.000000           0.000000                0.000000   \n",
       "max        3.000000     10.000000           1.000000               26.000000   \n",
       "\n",
       "       previous_bookings_not_canceled  booking_changes  days_in_waiting_list  \\\n",
       "count                    65229.000000     65229.000000          65229.000000   \n",
       "mean                         0.107713         0.194269              3.334422   \n",
       "std                          1.326638         0.593838             18.239606   \n",
       "min                          0.000000         0.000000              0.000000   \n",
       "25%                          0.000000         0.000000              0.000000   \n",
       "50%                          0.000000         0.000000              0.000000   \n",
       "75%                          0.000000         0.000000              0.000000   \n",
       "max                         58.000000        17.000000            259.000000   \n",
       "\n",
       "       required_car_parking_spaces  total_of_special_requests  total_nights  \n",
       "count                 65229.000000               65229.000000  65229.000000  \n",
       "mean                      0.056647                   0.512763      3.049978  \n",
       "std                       0.232094                   0.752590      1.738108  \n",
       "min                       0.000000                   0.000000      1.000000  \n",
       "25%                       0.000000                   0.000000      2.000000  \n",
       "50%                       0.000000                   0.000000      3.000000  \n",
       "75%                       0.000000                   1.000000      4.000000  \n",
       "max                       3.000000                   5.000000     10.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>meal</th>\n",
       "      <th>country</th>\n",
       "      <th>distribution_channel</th>\n",
       "      <th>reserved_room_type</th>\n",
       "      <th>customer_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>65229</td>\n",
       "      <td>65229</td>\n",
       "      <td>65229</td>\n",
       "      <td>65229</td>\n",
       "      <td>65229</td>\n",
       "      <td>65229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>155</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>October</td>\n",
       "      <td>BB</td>\n",
       "      <td>PRT</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>A</td>\n",
       "      <td>Transient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>9255</td>\n",
       "      <td>51697</td>\n",
       "      <td>28831</td>\n",
       "      <td>54454</td>\n",
       "      <td>50501</td>\n",
       "      <td>45493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       arrival_date_month       meal country distribution_channel  \\\n",
       "count               65229      65229   65229                65229   \n",
       "unique                 12          5     155                    4   \n",
       "top               October  BB            PRT                TA/TO   \n",
       "freq                 9255      51697   28831                54454   \n",
       "\n",
       "       reserved_room_type customer_type  \n",
       "count               65229         65229  \n",
       "unique                  7             4  \n",
       "top      A                    Transient  \n",
       "freq                50501         45493  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_i(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32412, 25)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32412 entries, 0 to 32411\n",
      "Data columns (total 25 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   id                              32412 non-null  int64  \n",
      " 1   is_canceled                     32412 non-null  int64  \n",
      " 2   lead_time                       32412 non-null  float64\n",
      " 3   arrival_date_year               32412 non-null  int64  \n",
      " 4   arrival_date_month              32412 non-null  object \n",
      " 5   arrival_date_week_number        32412 non-null  int64  \n",
      " 6   arrival_date_day_of_month       32412 non-null  int64  \n",
      " 7   stays_in_weekend_nights         32412 non-null  int64  \n",
      " 8   stays_in_week_nights            32412 non-null  int64  \n",
      " 9   adults                          32412 non-null  float64\n",
      " 10  children                        32412 non-null  float64\n",
      " 11  babies                          32412 non-null  float64\n",
      " 12  meal                            32412 non-null  object \n",
      " 13  country                         32412 non-null  object \n",
      " 14  distribution_channel            32412 non-null  object \n",
      " 15  is_repeated_guest               32412 non-null  int64  \n",
      " 16  previous_cancellations          32412 non-null  int64  \n",
      " 17  previous_bookings_not_canceled  32412 non-null  int64  \n",
      " 18  reserved_room_type              32412 non-null  object \n",
      " 19  booking_changes                 32412 non-null  int64  \n",
      " 20  days_in_waiting_list            32412 non-null  int64  \n",
      " 21  customer_type                   32412 non-null  object \n",
      " 22  required_car_parking_spaces     32412 non-null  int64  \n",
      " 23  total_of_special_requests       32412 non-null  int64  \n",
      " 24  total_nights                    32412 non-null  int64  \n",
      "dtypes: float64(4), int64(15), object(6)\n",
      "memory usage: 6.2+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>arrival_date_week_number</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>...</th>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <th>previous_cancellations</th>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <th>reserved_room_type</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>days_in_waiting_list</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6086</td>\n",
       "      <td>1</td>\n",
       "      <td>74.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6087</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6088</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6089</td>\n",
       "      <td>1</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6090</td>\n",
       "      <td>1</td>\n",
       "      <td>172.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0  6086            1       74.0               2017            January   \n",
       "1  6087            1       62.0               2017            January   \n",
       "2  6088            1       62.0               2017            January   \n",
       "3  6089            1       71.0               2017            January   \n",
       "4  6090            1      172.0               2017            January   \n",
       "\n",
       "   arrival_date_week_number  arrival_date_day_of_month  \\\n",
       "0                         1                          1   \n",
       "1                         1                          1   \n",
       "2                         1                          1   \n",
       "3                         1                          1   \n",
       "4                         1                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  ...  \\\n",
       "0                        1                     0     2.0  ...   \n",
       "1                        2                     2     2.0  ...   \n",
       "2                        2                     2     2.0  ...   \n",
       "3                        2                     2     1.0  ...   \n",
       "4                        2                     5     2.0  ...   \n",
       "\n",
       "   is_repeated_guest  previous_cancellations previous_bookings_not_canceled  \\\n",
       "0                  0                       0                              0   \n",
       "1                  0                       0                              0   \n",
       "2                  0                       0                              0   \n",
       "3                  0                       0                              0   \n",
       "4                  0                       0                              0   \n",
       "\n",
       "  reserved_room_type booking_changes  days_in_waiting_list  customer_type  \\\n",
       "0   A                              0                     0      Transient   \n",
       "1   A                              0                     0      Transient   \n",
       "2   A                              0                     0      Transient   \n",
       "3   A                              0                     0      Transient   \n",
       "4   A                              0                     0      Transient   \n",
       "\n",
       "   required_car_parking_spaces total_of_special_requests  total_nights  \n",
       "0                            0                         0             1  \n",
       "1                            0                         1             4  \n",
       "2                            0                         1             4  \n",
       "3                            0                         1             4  \n",
       "4                            0                         0             7  \n",
       "\n",
       "[5 rows x 25 columns]"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_week_number</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>count</th>\n",
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       "      <td>32412.000000</td>\n",
       "      <td>32412.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>60131.505183</td>\n",
       "      <td>0.388467</td>\n",
       "      <td>97.587869</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>17.804054</td>\n",
       "      <td>15.656948</td>\n",
       "      <td>0.867888</td>\n",
       "      <td>2.34009</td>\n",
       "      <td>1.858602</td>\n",
       "      <td>0.083765</td>\n",
       "      <td>0.005523</td>\n",
       "      <td>0.031377</td>\n",
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       "      <td>0.171418</td>\n",
       "      <td>0.216741</td>\n",
       "      <td>0.262341</td>\n",
       "      <td>0.046248</td>\n",
       "      <td>0.657781</td>\n",
       "      <td>3.207979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29953.586177</td>\n",
       "      <td>0.487409</td>\n",
       "      <td>86.507146</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.177384</td>\n",
       "      <td>8.766429</td>\n",
       "      <td>0.853394</td>\n",
       "      <td>1.37517</td>\n",
       "      <td>0.489341</td>\n",
       "      <td>0.343470</td>\n",
       "      <td>0.075757</td>\n",
       "      <td>0.174338</td>\n",
       "      <td>0.180473</td>\n",
       "      <td>1.875170</td>\n",
       "      <td>0.640551</td>\n",
       "      <td>4.733027</td>\n",
       "      <td>0.219367</td>\n",
       "      <td>0.834341</td>\n",
       "      <td>1.738683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6086.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>45291.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>53394.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>18.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>89800.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>25.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
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       "      <th>max</th>\n",
       "      <td>97903.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id   is_canceled     lead_time  arrival_date_year  \\\n",
       "count  32412.000000  32412.000000  32412.000000            32412.0   \n",
       "mean   60131.505183      0.388467     97.587869             2017.0   \n",
       "std    29953.586177      0.487409     86.507146                0.0   \n",
       "min     6086.000000      0.000000      0.000000             2017.0   \n",
       "25%    45291.750000      0.000000     23.000000             2017.0   \n",
       "50%    53394.500000      0.000000     76.000000             2017.0   \n",
       "75%    89800.250000      1.000000    155.000000             2017.0   \n",
       "max    97903.000000      1.000000    373.000000             2017.0   \n",
       "\n",
       "       arrival_date_week_number  arrival_date_day_of_month  \\\n",
       "count              32412.000000               32412.000000   \n",
       "mean                  17.804054                  15.656948   \n",
       "std                    9.177384                   8.766429   \n",
       "min                    1.000000                   1.000000   \n",
       "25%                   10.000000                   8.000000   \n",
       "50%                   18.000000                  15.500000   \n",
       "75%                   25.000000                  23.000000   \n",
       "max                   35.000000                  31.000000   \n",
       "\n",
       "       stays_in_weekend_nights  stays_in_week_nights        adults  \\\n",
       "count             32412.000000           32412.00000  32412.000000   \n",
       "mean                  0.867888               2.34009      1.858602   \n",
       "std                   0.853394               1.37517      0.489341   \n",
       "min                   0.000000               0.00000      0.000000   \n",
       "25%                   0.000000               1.00000      2.000000   \n",
       "50%                   1.000000               2.00000      2.000000   \n",
       "75%                   2.000000               3.00000      2.000000   \n",
       "max                   4.000000               6.00000      4.000000   \n",
       "\n",
       "           children        babies  is_repeated_guest  previous_cancellations  \\\n",
       "count  32412.000000  32412.000000       32412.000000            32412.000000   \n",
       "mean       0.083765      0.005523           0.031377                0.011601   \n",
       "std        0.343470      0.075757           0.174338                0.180473   \n",
       "min        0.000000      0.000000           0.000000                0.000000   \n",
       "25%        0.000000      0.000000           0.000000                0.000000   \n",
       "50%        0.000000      0.000000           0.000000                0.000000   \n",
       "75%        0.000000      0.000000           0.000000                0.000000   \n",
       "max        3.000000      2.000000           1.000000                6.000000   \n",
       "\n",
       "       previous_bookings_not_canceled  booking_changes  days_in_waiting_list  \\\n",
       "count                    32412.000000     32412.000000          32412.000000   \n",
       "mean                         0.171418         0.216741              0.262341   \n",
       "std                          1.875170         0.640551              4.733027   \n",
       "min                          0.000000         0.000000              0.000000   \n",
       "25%                          0.000000         0.000000              0.000000   \n",
       "50%                          0.000000         0.000000              0.000000   \n",
       "75%                          0.000000         0.000000              0.000000   \n",
       "max                         72.000000        18.000000            223.000000   \n",
       "\n",
       "       required_car_parking_spaces  total_of_special_requests  total_nights  \n",
       "count                 32412.000000               32412.000000  32412.000000  \n",
       "mean                      0.046248                   0.657781      3.207979  \n",
       "std                       0.219367                   0.834341      1.738683  \n",
       "min                       0.000000                   0.000000      1.000000  \n",
       "25%                       0.000000                   0.000000      2.000000  \n",
       "50%                       0.000000                   0.000000      3.000000  \n",
       "75%                       0.000000                   1.000000      4.000000  \n",
       "max                       8.000000                   5.000000     10.000000  "
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       "      <th>arrival_date_month</th>\n",
       "      <th>meal</th>\n",
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       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>May</td>\n",
       "      <td>BB</td>\n",
       "      <td>PRT</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>A</td>\n",
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       "      <th>freq</th>\n",
       "      <td>5262</td>\n",
       "      <td>24684</td>\n",
       "      <td>9887</td>\n",
       "      <td>27083</td>\n",
       "      <td>23471</td>\n",
       "      <td>27461</td>\n",
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      "text/plain": [
       "       arrival_date_month       meal country distribution_channel  \\\n",
       "count               32412      32412   32412                32412   \n",
       "unique                  8          5     143                    4   \n",
       "top                   May  BB            PRT                TA/TO   \n",
       "freq                 5262      24684    9887                27083   \n",
       "\n",
       "       reserved_room_type customer_type  \n",
       "count               32412         32412  \n",
       "unique                  7             4  \n",
       "top      A                    Transient  \n",
       "freq                23471         27461  "
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "show_i(df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Вывод:` - Тренировочный датасет в два раза больше тестового, это положительно скажется на обучении, много материала.\n",
    "\n",
    "Предварительное ознакомление не выявило пропущенных значений, но есть признаки которые, возможно, нуждаются в смене типа.\n",
    "Например: 'lead_time', 'adults', 'children' и 'babies'. Эти значения могут быть переведены из вещественных в цельночисловые (float -> int)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.2. <a id='toc1_1_2_'></a>[Шаг 2. Предобработка и исследовательский анализ данных](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1.2.1. <a id='toc1_1_2_1_'></a>[Предобработка данных](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим типы данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                                  int64\n",
      "is_canceled                         int64\n",
      "lead_time                         float64\n",
      "arrival_date_year                   int64\n",
      "arrival_date_month                 object\n",
      "arrival_date_week_number            int64\n",
      "arrival_date_day_of_month           int64\n",
      "stays_in_weekend_nights             int64\n",
      "stays_in_week_nights                int64\n",
      "adults                            float64\n",
      "children                          float64\n",
      "babies                            float64\n",
      "meal                               object\n",
      "country                            object\n",
      "distribution_channel               object\n",
      "is_repeated_guest                   int64\n",
      "previous_cancellations              int64\n",
      "previous_bookings_not_canceled      int64\n",
      "reserved_room_type                 object\n",
      "booking_changes                     int64\n",
      "days_in_waiting_list                int64\n",
      "customer_type                      object\n",
      "required_car_parking_spaces         int64\n",
      "total_of_special_requests           int64\n",
      "total_nights                        int64\n",
      "dtype: object\n"
     ]
    },
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BB</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Direct</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BB</td>\n",
       "      <td>GBR</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BB</td>\n",
       "      <td>PRT</td>\n",
       "      <td>Direct</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FB</td>\n",
       "      <td>PRT</td>\n",
       "      <td>Direct</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BB</td>\n",
       "      <td>PRT</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0   0            0        7.0               2015               July   \n",
       "1   1            0       14.0               2015               July   \n",
       "2   2            0        0.0               2015               July   \n",
       "3   3            0        9.0               2015               July   \n",
       "4   4            1       85.0               2015               July   \n",
       "\n",
       "   arrival_date_week_number  arrival_date_day_of_month  \\\n",
       "0                        27                          1   \n",
       "1                        27                          1   \n",
       "2                        27                          1   \n",
       "3                        27                          1   \n",
       "4                        27                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  children  babies  \\\n",
       "0                        0                     1     1.0       0.0     0.0   \n",
       "1                        0                     2     2.0       0.0     0.0   \n",
       "2                        0                     2     2.0       0.0     0.0   \n",
       "3                        0                     2     2.0       0.0     0.0   \n",
       "4                        0                     3     2.0       0.0     0.0   \n",
       "\n",
       "        meal country distribution_channel  is_repeated_guest  \\\n",
       "0  BB            GBR               Direct                  0   \n",
       "1  BB            GBR                TA/TO                  0   \n",
       "2  BB            PRT               Direct                  0   \n",
       "3  FB            PRT               Direct                  0   \n",
       "4  BB            PRT                TA/TO                  0   \n",
       "\n",
       "   previous_cancellations  previous_bookings_not_canceled reserved_room_type  \\\n",
       "0                       0                               0   A                  \n",
       "1                       0                               0   A                  \n",
       "2                       0                               0   C                  \n",
       "3                       0                               0   C                  \n",
       "4                       0                               0   A                  \n",
       "\n",
       "   booking_changes  days_in_waiting_list customer_type  \\\n",
       "0                0                     0     Transient   \n",
       "1                0                     0     Transient   \n",
       "2                0                     0     Transient   \n",
       "3                0                     0     Transient   \n",
       "4                0                     0     Transient   \n",
       "\n",
       "   required_car_parking_spaces  total_of_special_requests  total_nights  \n",
       "0                            0                          0             1  \n",
       "1                            0                          1             2  \n",
       "2                            0                          0             2  \n",
       "3                            0                          1             2  \n",
       "4                            0                          1             3  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# выборка train\n",
    "print(df_train.dtypes)\n",
    "\n",
    "# выводим все колонки без пропусков для лучшего обзора\n",
    "pd.set_option('display.max_columns', None)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                                  int64\n",
      "is_canceled                         int64\n",
      "lead_time                         float64\n",
      "arrival_date_year                   int64\n",
      "arrival_date_month                 object\n",
      "arrival_date_week_number            int64\n",
      "arrival_date_day_of_month           int64\n",
      "stays_in_weekend_nights             int64\n",
      "stays_in_week_nights                int64\n",
      "adults                            float64\n",
      "children                          float64\n",
      "babies                            float64\n",
      "meal                               object\n",
      "country                            object\n",
      "distribution_channel               object\n",
      "is_repeated_guest                   int64\n",
      "previous_cancellations              int64\n",
      "previous_bookings_not_canceled      int64\n",
      "reserved_room_type                 object\n",
      "booking_changes                     int64\n",
      "days_in_waiting_list                int64\n",
      "customer_type                      object\n",
      "required_car_parking_spaces         int64\n",
      "total_of_special_requests           int64\n",
      "total_nights                        int64\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>arrival_date_week_number</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>babies</th>\n",
       "      <th>meal</th>\n",
       "      <th>country</th>\n",
       "      <th>distribution_channel</th>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <th>previous_cancellations</th>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <th>reserved_room_type</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>total_nights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>6086</td>\n",
       "      <td>1</td>\n",
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       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BB</td>\n",
       "      <td>PRT</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6087</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>AUT</td>\n",
       "      <td>TA/TO</td>\n",
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       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6088</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BB</td>\n",
       "      <td>AUT</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6089</td>\n",
       "      <td>1</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BB</td>\n",
       "      <td>PRT</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6090</td>\n",
       "      <td>1</td>\n",
       "      <td>172.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BB</td>\n",
       "      <td>BEL</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0  6086            1       74.0               2017            January   \n",
       "1  6087            1       62.0               2017            January   \n",
       "2  6088            1       62.0               2017            January   \n",
       "3  6089            1       71.0               2017            January   \n",
       "4  6090            1      172.0               2017            January   \n",
       "\n",
       "   arrival_date_week_number  arrival_date_day_of_month  \\\n",
       "0                         1                          1   \n",
       "1                         1                          1   \n",
       "2                         1                          1   \n",
       "3                         1                          1   \n",
       "4                         1                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  children  babies  \\\n",
       "0                        1                     0     2.0       0.0     0.0   \n",
       "1                        2                     2     2.0       0.0     0.0   \n",
       "2                        2                     2     2.0       0.0     0.0   \n",
       "3                        2                     2     1.0       0.0     0.0   \n",
       "4                        2                     5     2.0       0.0     0.0   \n",
       "\n",
       "        meal country distribution_channel  is_repeated_guest  \\\n",
       "0  BB            PRT                TA/TO                  0   \n",
       "1  BB            AUT                TA/TO                  0   \n",
       "2  BB            AUT                TA/TO                  0   \n",
       "3  BB            PRT                TA/TO                  0   \n",
       "4  BB            BEL                TA/TO                  0   \n",
       "\n",
       "   previous_cancellations  previous_bookings_not_canceled reserved_room_type  \\\n",
       "0                       0                               0   A                  \n",
       "1                       0                               0   A                  \n",
       "2                       0                               0   A                  \n",
       "3                       0                               0   A                  \n",
       "4                       0                               0   A                  \n",
       "\n",
       "   booking_changes  days_in_waiting_list customer_type  \\\n",
       "0                0                     0     Transient   \n",
       "1                0                     0     Transient   \n",
       "2                0                     0     Transient   \n",
       "3                0                     0     Transient   \n",
       "4                0                     0     Transient   \n",
       "\n",
       "   required_car_parking_spaces  total_of_special_requests  total_nights  \n",
       "0                            0                          0             1  \n",
       "1                            0                          1             4  \n",
       "2                            0                          1             4  \n",
       "3                            0                          1             4  \n",
       "4                            0                          0             7  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# выборка test\n",
    "print(df_test.dtypes)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В обоих датафреймах есть признаки описывающие цельные значения, например, количество детей, но их тип указан как вещественный.\n",
    "Переведём вещественные признаки в цельночисловые."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# меняем тип float -> int в df_train\n",
    "df_train['lead_time'] = df_train['lead_time'].astype(int)\n",
    "df_train['adults'] = df_train['adults'].astype(int)\n",
    "df_train['children'] = df_train['children'].astype(int)\n",
    "df_train['babies'] = df_train['babies'].astype(int)\n",
    "\n",
    "# меняем тип float -> int в df_test\n",
    "df_test['lead_time'] = df_test['lead_time'].astype(int)\n",
    "df_test['adults'] = df_test['adults'].astype(int)\n",
    "df_test['children'] = df_test['children'].astype(int)\n",
    "df_test['babies'] = df_test['babies'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверяем что типы поменялись на Int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                                 int64\n",
      "is_canceled                        int64\n",
      "lead_time                          int64\n",
      "arrival_date_year                  int64\n",
      "arrival_date_month                object\n",
      "arrival_date_week_number           int64\n",
      "arrival_date_day_of_month          int64\n",
      "stays_in_weekend_nights            int64\n",
      "stays_in_week_nights               int64\n",
      "adults                             int64\n",
      "children                           int64\n",
      "babies                             int64\n",
      "meal                              object\n",
      "country                           object\n",
      "distribution_channel              object\n",
      "is_repeated_guest                  int64\n",
      "previous_cancellations             int64\n",
      "previous_bookings_not_canceled     int64\n",
      "reserved_room_type                object\n",
      "booking_changes                    int64\n",
      "days_in_waiting_list               int64\n",
      "customer_type                     object\n",
      "required_car_parking_spaces        int64\n",
      "total_of_special_requests          int64\n",
      "total_nights                       int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df_train.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                                 int64\n",
      "is_canceled                        int64\n",
      "lead_time                          int64\n",
      "arrival_date_year                  int64\n",
      "arrival_date_month                object\n",
      "arrival_date_week_number           int64\n",
      "arrival_date_day_of_month          int64\n",
      "stays_in_weekend_nights            int64\n",
      "stays_in_week_nights               int64\n",
      "adults                             int64\n",
      "children                           int64\n",
      "babies                             int64\n",
      "meal                              object\n",
      "country                           object\n",
      "distribution_channel              object\n",
      "is_repeated_guest                  int64\n",
      "previous_cancellations             int64\n",
      "previous_bookings_not_canceled     int64\n",
      "reserved_room_type                object\n",
      "booking_changes                    int64\n",
      "days_in_waiting_list               int64\n",
      "customer_type                     object\n",
      "required_car_parking_spaces        int64\n",
      "total_of_special_requests          int64\n",
      "total_nights                       int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df_test.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Вывод:` - типы данных успешно изменены."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверяем пропущенные значения в таблицах в процентах"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'df_train missing data:'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_47217_row0_col0,#T_47217_row1_col0,#T_47217_row2_col0,#T_47217_row3_col0,#T_47217_row4_col0,#T_47217_row5_col0,#T_47217_row6_col0,#T_47217_row7_col0,#T_47217_row8_col0,#T_47217_row9_col0,#T_47217_row10_col0,#T_47217_row11_col0,#T_47217_row12_col0,#T_47217_row13_col0,#T_47217_row14_col0,#T_47217_row15_col0,#T_47217_row16_col0,#T_47217_row17_col0,#T_47217_row18_col0,#T_47217_row19_col0,#T_47217_row20_col0,#T_47217_row21_col0,#T_47217_row22_col0,#T_47217_row23_col0,#T_47217_row24_col0{\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_47217_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_47217_level0_row0\" class=\"row_heading level0 row0\" >id</th>\n",
       "                        <td id=\"T_47217_row0_col0\" class=\"data row0 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row1\" class=\"row_heading level0 row1\" >is_canceled</th>\n",
       "                        <td id=\"T_47217_row1_col0\" class=\"data row1 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row2\" class=\"row_heading level0 row2\" >lead_time</th>\n",
       "                        <td id=\"T_47217_row2_col0\" class=\"data row2 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row3\" class=\"row_heading level0 row3\" >arrival_date_year</th>\n",
       "                        <td id=\"T_47217_row3_col0\" class=\"data row3 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row4\" class=\"row_heading level0 row4\" >arrival_date_month</th>\n",
       "                        <td id=\"T_47217_row4_col0\" class=\"data row4 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row5\" class=\"row_heading level0 row5\" >arrival_date_week_number</th>\n",
       "                        <td id=\"T_47217_row5_col0\" class=\"data row5 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row6\" class=\"row_heading level0 row6\" >arrival_date_day_of_month</th>\n",
       "                        <td id=\"T_47217_row6_col0\" class=\"data row6 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row7\" class=\"row_heading level0 row7\" >stays_in_weekend_nights</th>\n",
       "                        <td id=\"T_47217_row7_col0\" class=\"data row7 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row8\" class=\"row_heading level0 row8\" >stays_in_week_nights</th>\n",
       "                        <td id=\"T_47217_row8_col0\" class=\"data row8 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row9\" class=\"row_heading level0 row9\" >adults</th>\n",
       "                        <td id=\"T_47217_row9_col0\" class=\"data row9 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row10\" class=\"row_heading level0 row10\" >children</th>\n",
       "                        <td id=\"T_47217_row10_col0\" class=\"data row10 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row11\" class=\"row_heading level0 row11\" >babies</th>\n",
       "                        <td id=\"T_47217_row11_col0\" class=\"data row11 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row12\" class=\"row_heading level0 row12\" >meal</th>\n",
       "                        <td id=\"T_47217_row12_col0\" class=\"data row12 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row13\" class=\"row_heading level0 row13\" >country</th>\n",
       "                        <td id=\"T_47217_row13_col0\" class=\"data row13 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row14\" class=\"row_heading level0 row14\" >distribution_channel</th>\n",
       "                        <td id=\"T_47217_row14_col0\" class=\"data row14 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row15\" class=\"row_heading level0 row15\" >is_repeated_guest</th>\n",
       "                        <td id=\"T_47217_row15_col0\" class=\"data row15 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row16\" class=\"row_heading level0 row16\" >previous_cancellations</th>\n",
       "                        <td id=\"T_47217_row16_col0\" class=\"data row16 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row17\" class=\"row_heading level0 row17\" >previous_bookings_not_canceled</th>\n",
       "                        <td id=\"T_47217_row17_col0\" class=\"data row17 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row18\" class=\"row_heading level0 row18\" >reserved_room_type</th>\n",
       "                        <td id=\"T_47217_row18_col0\" class=\"data row18 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row19\" class=\"row_heading level0 row19\" >booking_changes</th>\n",
       "                        <td id=\"T_47217_row19_col0\" class=\"data row19 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row20\" class=\"row_heading level0 row20\" >days_in_waiting_list</th>\n",
       "                        <td id=\"T_47217_row20_col0\" class=\"data row20 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row21\" class=\"row_heading level0 row21\" >customer_type</th>\n",
       "                        <td id=\"T_47217_row21_col0\" class=\"data row21 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row22\" class=\"row_heading level0 row22\" >required_car_parking_spaces</th>\n",
       "                        <td id=\"T_47217_row22_col0\" class=\"data row22 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row23\" class=\"row_heading level0 row23\" >total_of_special_requests</th>\n",
       "                        <td id=\"T_47217_row23_col0\" class=\"data row23 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_47217_level0_row24\" class=\"row_heading level0 row24\" >total_nights</th>\n",
       "                        <td id=\"T_47217_row24_col0\" class=\"data row24 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f55c0a04910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'df_test missing data:'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_d6b39_row0_col0,#T_d6b39_row1_col0,#T_d6b39_row2_col0,#T_d6b39_row3_col0,#T_d6b39_row4_col0,#T_d6b39_row5_col0,#T_d6b39_row6_col0,#T_d6b39_row7_col0,#T_d6b39_row8_col0,#T_d6b39_row9_col0,#T_d6b39_row10_col0,#T_d6b39_row11_col0,#T_d6b39_row12_col0,#T_d6b39_row13_col0,#T_d6b39_row14_col0,#T_d6b39_row15_col0,#T_d6b39_row16_col0,#T_d6b39_row17_col0,#T_d6b39_row18_col0,#T_d6b39_row19_col0,#T_d6b39_row20_col0,#T_d6b39_row21_col0,#T_d6b39_row22_col0,#T_d6b39_row23_col0,#T_d6b39_row24_col0{\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_d6b39_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_d6b39_level0_row0\" class=\"row_heading level0 row0\" >id</th>\n",
       "                        <td id=\"T_d6b39_row0_col0\" class=\"data row0 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row1\" class=\"row_heading level0 row1\" >is_canceled</th>\n",
       "                        <td id=\"T_d6b39_row1_col0\" class=\"data row1 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row2\" class=\"row_heading level0 row2\" >lead_time</th>\n",
       "                        <td id=\"T_d6b39_row2_col0\" class=\"data row2 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row3\" class=\"row_heading level0 row3\" >arrival_date_year</th>\n",
       "                        <td id=\"T_d6b39_row3_col0\" class=\"data row3 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row4\" class=\"row_heading level0 row4\" >arrival_date_month</th>\n",
       "                        <td id=\"T_d6b39_row4_col0\" class=\"data row4 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row5\" class=\"row_heading level0 row5\" >arrival_date_week_number</th>\n",
       "                        <td id=\"T_d6b39_row5_col0\" class=\"data row5 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row6\" class=\"row_heading level0 row6\" >arrival_date_day_of_month</th>\n",
       "                        <td id=\"T_d6b39_row6_col0\" class=\"data row6 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row7\" class=\"row_heading level0 row7\" >stays_in_weekend_nights</th>\n",
       "                        <td id=\"T_d6b39_row7_col0\" class=\"data row7 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row8\" class=\"row_heading level0 row8\" >stays_in_week_nights</th>\n",
       "                        <td id=\"T_d6b39_row8_col0\" class=\"data row8 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row9\" class=\"row_heading level0 row9\" >adults</th>\n",
       "                        <td id=\"T_d6b39_row9_col0\" class=\"data row9 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row10\" class=\"row_heading level0 row10\" >children</th>\n",
       "                        <td id=\"T_d6b39_row10_col0\" class=\"data row10 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row11\" class=\"row_heading level0 row11\" >babies</th>\n",
       "                        <td id=\"T_d6b39_row11_col0\" class=\"data row11 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row12\" class=\"row_heading level0 row12\" >meal</th>\n",
       "                        <td id=\"T_d6b39_row12_col0\" class=\"data row12 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row13\" class=\"row_heading level0 row13\" >country</th>\n",
       "                        <td id=\"T_d6b39_row13_col0\" class=\"data row13 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row14\" class=\"row_heading level0 row14\" >distribution_channel</th>\n",
       "                        <td id=\"T_d6b39_row14_col0\" class=\"data row14 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row15\" class=\"row_heading level0 row15\" >is_repeated_guest</th>\n",
       "                        <td id=\"T_d6b39_row15_col0\" class=\"data row15 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row16\" class=\"row_heading level0 row16\" >previous_cancellations</th>\n",
       "                        <td id=\"T_d6b39_row16_col0\" class=\"data row16 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row17\" class=\"row_heading level0 row17\" >previous_bookings_not_canceled</th>\n",
       "                        <td id=\"T_d6b39_row17_col0\" class=\"data row17 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row18\" class=\"row_heading level0 row18\" >reserved_room_type</th>\n",
       "                        <td id=\"T_d6b39_row18_col0\" class=\"data row18 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row19\" class=\"row_heading level0 row19\" >booking_changes</th>\n",
       "                        <td id=\"T_d6b39_row19_col0\" class=\"data row19 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row20\" class=\"row_heading level0 row20\" >days_in_waiting_list</th>\n",
       "                        <td id=\"T_d6b39_row20_col0\" class=\"data row20 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row21\" class=\"row_heading level0 row21\" >customer_type</th>\n",
       "                        <td id=\"T_d6b39_row21_col0\" class=\"data row21 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row22\" class=\"row_heading level0 row22\" >required_car_parking_spaces</th>\n",
       "                        <td id=\"T_d6b39_row22_col0\" class=\"data row22 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row23\" class=\"row_heading level0 row23\" >total_of_special_requests</th>\n",
       "                        <td id=\"T_d6b39_row23_col0\" class=\"data row23 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d6b39_level0_row24\" class=\"row_heading level0 row24\" >total_nights</th>\n",
       "                        <td id=\"T_d6b39_row24_col0\" class=\"data row24 col0\" >0.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f55bfb715b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df_train пропуски\n",
    "display('df_train missing data:', pd.DataFrame(round(df_train.isna().mean()*100)).style.background_gradient('coolwarm'))\n",
    "\n",
    "# df_test пропуски\n",
    "display('df_test missing data:', pd.DataFrame(round(df_test.isna().mean()*100)).style.background_gradient('coolwarm'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Вывод:` - пропущенных значений нет."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проеверяем наличие явных дубликатов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_train дубликаты: 0\n",
      "df_test дубликаты: 0\n"
     ]
    }
   ],
   "source": [
    "# Проверяем очевидные дубликаты - строки которые полностью повторяют уже встречавшиеся в датасете. Очевидных дубликатов нет.\n",
    "\n",
    "# df_train дубликаты\n",
    "print('df_train дубликаты:', df_train.duplicated().sum())\n",
    "\n",
    "# df_test дубликаты\n",
    "print('df_test дубликаты:', df_test.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Вывод:` - дубликатов нет"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Убраем колонки которые нам не нужны для обучения. Колонка `id` не несёт иформацинной нагрузки, параметр `arrival_date_week_number` также не является информативным, более того, он отчасти дублирует другие временные параметры имеющиеся в нашем распоряжении, например `arrival_date_day_of_month`. Параметр `country` по-сути влияет на параметр `lead_time` тк удалённость страны может влиять на продолжительность времени между бронированием и прибитием в отель."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train.drop(columns=['id', 'arrival_date_week_number', 'country'], axis=1)\n",
    "df_test = df_test.drop(columns=['id', 'arrival_date_week_number', 'country'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        vertical-align: top;\n",
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       "\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>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>babies</th>\n",
       "      <th>meal</th>\n",
       "      <th>distribution_channel</th>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <th>previous_cancellations</th>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <th>reserved_room_type</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>total_nights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>FB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65224</th>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>2016</td>\n",
       "      <td>December</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65225</th>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>2016</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65226</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2016</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65227</th>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>2016</td>\n",
       "      <td>December</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SC</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65228</th>\n",
       "      <td>0</td>\n",
       "      <td>107</td>\n",
       "      <td>2016</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65229 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0                0          7               2015               July   \n",
       "1                0         14               2015               July   \n",
       "2                0          0               2015               July   \n",
       "3                0          9               2015               July   \n",
       "4                1         85               2015               July   \n",
       "...            ...        ...                ...                ...   \n",
       "65224            0         23               2016           December   \n",
       "65225            0         53               2016           December   \n",
       "65226            0          7               2016           December   \n",
       "65227            0         17               2016           December   \n",
       "65228            0        107               2016           December   \n",
       "\n",
       "       arrival_date_day_of_month  stays_in_weekend_nights  \\\n",
       "0                              1                        0   \n",
       "1                              1                        0   \n",
       "2                              1                        0   \n",
       "3                              1                        0   \n",
       "4                              1                        0   \n",
       "...                          ...                      ...   \n",
       "65224                         30                        2   \n",
       "65225                         31                        2   \n",
       "65226                         31                        2   \n",
       "65227                         30                        2   \n",
       "65228                         31                        2   \n",
       "\n",
       "       stays_in_week_nights  adults  children  babies       meal  \\\n",
       "0                         1       1         0       0  BB          \n",
       "1                         2       2         0       0  BB          \n",
       "2                         2       2         0       0  BB          \n",
       "3                         2       2         0       0  FB          \n",
       "4                         3       2         0       0  BB          \n",
       "...                     ...     ...       ...     ...        ...   \n",
       "65224                     4       2         0       0  BB          \n",
       "65225                     3       2         0       0  HB          \n",
       "65226                     4       2         0       0  BB          \n",
       "65227                     5       2         0       0  SC          \n",
       "65228                     5       2         0       0  BB          \n",
       "\n",
       "      distribution_channel  is_repeated_guest  previous_cancellations  \\\n",
       "0                   Direct                  0                       0   \n",
       "1                    TA/TO                  0                       0   \n",
       "2                   Direct                  0                       0   \n",
       "3                   Direct                  0                       0   \n",
       "4                    TA/TO                  0                       0   \n",
       "...                    ...                ...                     ...   \n",
       "65224                TA/TO                  0                       0   \n",
       "65225                TA/TO                  0                       0   \n",
       "65226                TA/TO                  0                       0   \n",
       "65227                TA/TO                  0                       0   \n",
       "65228                TA/TO                  0                       0   \n",
       "\n",
       "       previous_bookings_not_canceled reserved_room_type  booking_changes  \\\n",
       "0                                   0   A                               0   \n",
       "1                                   0   A                               0   \n",
       "2                                   0   C                               0   \n",
       "3                                   0   C                               0   \n",
       "4                                   0   A                               0   \n",
       "...                               ...                ...              ...   \n",
       "65224                               0   D                               0   \n",
       "65225                               0   D                               0   \n",
       "65226                               0   D                               0   \n",
       "65227                               0   A                               0   \n",
       "65228                               0   A                               0   \n",
       "\n",
       "       days_in_waiting_list customer_type  required_car_parking_spaces  \\\n",
       "0                         0     Transient                            0   \n",
       "1                         0     Transient                            0   \n",
       "2                         0     Transient                            0   \n",
       "3                         0     Transient                            0   \n",
       "4                         0     Transient                            0   \n",
       "...                     ...           ...                          ...   \n",
       "65224                     0     Transient                            0   \n",
       "65225                     0     Transient                            0   \n",
       "65226                     0     Transient                            0   \n",
       "65227                     0     Transient                            0   \n",
       "65228                     0     Transient                            0   \n",
       "\n",
       "       total_of_special_requests  total_nights  \n",
       "0                              0             1  \n",
       "1                              1             2  \n",
       "2                              0             2  \n",
       "3                              1             2  \n",
       "4                              1             3  \n",
       "...                          ...           ...  \n",
       "65224                          2             6  \n",
       "65225                          3             5  \n",
       "65226                          1             6  \n",
       "65227                          1             7  \n",
       "65228                          0             7  \n",
       "\n",
       "[65229 rows x 22 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверяем удаление"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['is_canceled', 'lead_time', 'arrival_date_year', 'arrival_date_month',\n",
      "       'arrival_date_day_of_month', 'stays_in_weekend_nights',\n",
      "       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',\n",
      "       'distribution_channel', 'is_repeated_guest', 'previous_cancellations',\n",
      "       'previous_bookings_not_canceled', 'reserved_room_type',\n",
      "       'booking_changes', 'days_in_waiting_list', 'customer_type',\n",
      "       'required_car_parking_spaces', 'total_of_special_requests',\n",
      "       'total_nights'],\n",
      "      dtype='object')\n",
      "Index(['is_canceled', 'lead_time', 'arrival_date_year', 'arrival_date_month',\n",
      "       'arrival_date_day_of_month', 'stays_in_weekend_nights',\n",
      "       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',\n",
      "       'distribution_channel', 'is_repeated_guest', 'previous_cancellations',\n",
      "       'previous_bookings_not_canceled', 'reserved_room_type',\n",
      "       'booking_changes', 'days_in_waiting_list', 'customer_type',\n",
      "       'required_car_parking_spaces', 'total_of_special_requests',\n",
      "       'total_nights'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df_train.columns)\n",
    "print(df_test.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Вывод после предобработки` - В результате предобработки были выявлены типы данных нуждающихся в изменении типа, смена типа произведена. Пропусков не найдело, дубликатов - тоже. Были удалены параметры не влияющие на анализ. Данные готовы к анализу. Целевым признаком является `is_canceled`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.3. <a id='toc1_1_3_'></a>[Исследовательский анализ данных](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Отфильтруем признаки с небинарными значениям чтобы вывести таблицу с корреляцией."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "features1=list(['is_canceled', 'lead_time','arrival_date_month','arrival_date_day_of_month', 'stays_in_weekend_nights', 'stays_in_week_nights',\n",
    " 'adults', 'children', 'babies', 'meal', 'distribution_channel','reserved_room_type', 'booking_changes', 'days_in_waiting_list', 'customer_type',\n",
    " 'required_car_parking_spaces', 'total_of_special_requests', 'total_nights'])\n",
    "\n",
    "\n",
    "features2=list(['is_canceled', 'lead_time','arrival_date_month','arrival_date_day_of_month', 'stays_in_weekend_nights', 'stays_in_week_nights',\n",
    " 'adults', 'children', 'babies', 'meal', 'distribution_channel','reserved_room_type', 'booking_changes', 'days_in_waiting_list', 'customer_type',\n",
    "  'required_car_parking_spaces', 'total_of_special_requests', 'total_nights'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выводим таблицу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>babies</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>total_nights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>is_canceled</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.328142</td>\n",
       "      <td>-0.001903</td>\n",
       "      <td>-0.013767</td>\n",
       "      <td>0.010650</td>\n",
       "      <td>0.045153</td>\n",
       "      <td>-0.017746</td>\n",
       "      <td>-0.029589</td>\n",
       "      <td>-0.142935</td>\n",
       "      <td>0.075848</td>\n",
       "      <td>-0.186486</td>\n",
       "      <td>-0.257061</td>\n",
       "      <td>0.001542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lead_time</th>\n",
       "      <td>0.328142</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010252</td>\n",
       "      <td>0.047003</td>\n",
       "      <td>0.172646</td>\n",
       "      <td>0.119617</td>\n",
       "      <td>-0.056359</td>\n",
       "      <td>-0.024649</td>\n",
       "      <td>-0.007191</td>\n",
       "      <td>0.209741</td>\n",
       "      <td>-0.116867</td>\n",
       "      <td>-0.147738</td>\n",
       "      <td>0.157671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <td>-0.001903</td>\n",
       "      <td>-0.010252</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002017</td>\n",
       "      <td>0.003705</td>\n",
       "      <td>0.006034</td>\n",
       "      <td>0.018558</td>\n",
       "      <td>-0.000482</td>\n",
       "      <td>0.022560</td>\n",
       "      <td>0.017070</td>\n",
       "      <td>0.003816</td>\n",
       "      <td>0.005875</td>\n",
       "      <td>0.001898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <td>-0.013767</td>\n",
       "      <td>0.047003</td>\n",
       "      <td>-0.002017</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197410</td>\n",
       "      <td>0.116679</td>\n",
       "      <td>0.026224</td>\n",
       "      <td>0.018822</td>\n",
       "      <td>0.023118</td>\n",
       "      <td>-0.065167</td>\n",
       "      <td>-0.015870</td>\n",
       "      <td>0.083682</td>\n",
       "      <td>0.644935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <td>0.010650</td>\n",
       "      <td>0.172646</td>\n",
       "      <td>0.003705</td>\n",
       "      <td>0.197410</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.119901</td>\n",
       "      <td>0.023590</td>\n",
       "      <td>0.018428</td>\n",
       "      <td>0.061721</td>\n",
       "      <td>0.036037</td>\n",
       "      <td>-0.026988</td>\n",
       "      <td>0.090359</td>\n",
       "      <td>0.876515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adults</th>\n",
       "      <td>0.045153</td>\n",
       "      <td>0.119617</td>\n",
       "      <td>0.006034</td>\n",
       "      <td>0.116679</td>\n",
       "      <td>0.119901</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003216</td>\n",
       "      <td>0.019756</td>\n",
       "      <td>-0.056396</td>\n",
       "      <td>-0.013475</td>\n",
       "      <td>0.013321</td>\n",
       "      <td>0.171793</td>\n",
       "      <td>0.150766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>-0.017746</td>\n",
       "      <td>-0.056359</td>\n",
       "      <td>0.018558</td>\n",
       "      <td>0.026224</td>\n",
       "      <td>0.023590</td>\n",
       "      <td>-0.003216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018410</td>\n",
       "      <td>0.053835</td>\n",
       "      <td>-0.038615</td>\n",
       "      <td>0.047881</td>\n",
       "      <td>0.112699</td>\n",
       "      <td>0.031268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>babies</th>\n",
       "      <td>-0.029589</td>\n",
       "      <td>-0.024649</td>\n",
       "      <td>-0.000482</td>\n",
       "      <td>0.018822</td>\n",
       "      <td>0.018428</td>\n",
       "      <td>0.019756</td>\n",
       "      <td>0.018410</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.076910</td>\n",
       "      <td>-0.013391</td>\n",
       "      <td>0.030264</td>\n",
       "      <td>0.098973</td>\n",
       "      <td>0.023608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>booking_changes</th>\n",
       "      <td>-0.142935</td>\n",
       "      <td>-0.007191</td>\n",
       "      <td>0.022560</td>\n",
       "      <td>0.023118</td>\n",
       "      <td>0.061721</td>\n",
       "      <td>-0.056396</td>\n",
       "      <td>0.053835</td>\n",
       "      <td>0.076910</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.011225</td>\n",
       "      <td>0.059084</td>\n",
       "      <td>0.043884</td>\n",
       "      <td>0.059468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <td>0.075848</td>\n",
       "      <td>0.209741</td>\n",
       "      <td>0.017070</td>\n",
       "      <td>-0.065167</td>\n",
       "      <td>0.036037</td>\n",
       "      <td>-0.013475</td>\n",
       "      <td>-0.038615</td>\n",
       "      <td>-0.013391</td>\n",
       "      <td>-0.011225</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.044083</td>\n",
       "      <td>-0.106080</td>\n",
       "      <td>-0.003906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <td>-0.186486</td>\n",
       "      <td>-0.116867</td>\n",
       "      <td>0.003816</td>\n",
       "      <td>-0.015870</td>\n",
       "      <td>-0.026988</td>\n",
       "      <td>0.013321</td>\n",
       "      <td>0.047881</td>\n",
       "      <td>0.030264</td>\n",
       "      <td>0.059084</td>\n",
       "      <td>-0.044083</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.087009</td>\n",
       "      <td>-0.028832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <td>-0.257061</td>\n",
       "      <td>-0.147738</td>\n",
       "      <td>0.005875</td>\n",
       "      <td>0.083682</td>\n",
       "      <td>0.090359</td>\n",
       "      <td>0.171793</td>\n",
       "      <td>0.112699</td>\n",
       "      <td>0.098973</td>\n",
       "      <td>0.043884</td>\n",
       "      <td>-0.106080</td>\n",
       "      <td>0.087009</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.111533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_nights</th>\n",
       "      <td>0.001542</td>\n",
       "      <td>0.157671</td>\n",
       "      <td>0.001898</td>\n",
       "      <td>0.644935</td>\n",
       "      <td>0.876515</td>\n",
       "      <td>0.150766</td>\n",
       "      <td>0.031268</td>\n",
       "      <td>0.023608</td>\n",
       "      <td>0.059468</td>\n",
       "      <td>-0.003906</td>\n",
       "      <td>-0.028832</td>\n",
       "      <td>0.111533</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             is_canceled  lead_time  \\\n",
       "is_canceled                     1.000000   0.328142   \n",
       "lead_time                       0.328142   1.000000   \n",
       "arrival_date_day_of_month      -0.001903  -0.010252   \n",
       "stays_in_weekend_nights        -0.013767   0.047003   \n",
       "stays_in_week_nights            0.010650   0.172646   \n",
       "adults                          0.045153   0.119617   \n",
       "children                       -0.017746  -0.056359   \n",
       "babies                         -0.029589  -0.024649   \n",
       "booking_changes                -0.142935  -0.007191   \n",
       "days_in_waiting_list            0.075848   0.209741   \n",
       "required_car_parking_spaces    -0.186486  -0.116867   \n",
       "total_of_special_requests      -0.257061  -0.147738   \n",
       "total_nights                    0.001542   0.157671   \n",
       "\n",
       "                             arrival_date_day_of_month  \\\n",
       "is_canceled                                  -0.001903   \n",
       "lead_time                                    -0.010252   \n",
       "arrival_date_day_of_month                     1.000000   \n",
       "stays_in_weekend_nights                      -0.002017   \n",
       "stays_in_week_nights                          0.003705   \n",
       "adults                                        0.006034   \n",
       "children                                      0.018558   \n",
       "babies                                       -0.000482   \n",
       "booking_changes                               0.022560   \n",
       "days_in_waiting_list                          0.017070   \n",
       "required_car_parking_spaces                   0.003816   \n",
       "total_of_special_requests                     0.005875   \n",
       "total_nights                                  0.001898   \n",
       "\n",
       "                             stays_in_weekend_nights  stays_in_week_nights  \\\n",
       "is_canceled                                -0.013767              0.010650   \n",
       "lead_time                                   0.047003              0.172646   \n",
       "arrival_date_day_of_month                  -0.002017              0.003705   \n",
       "stays_in_weekend_nights                     1.000000              0.197410   \n",
       "stays_in_week_nights                        0.197410              1.000000   \n",
       "adults                                      0.116679              0.119901   \n",
       "children                                    0.026224              0.023590   \n",
       "babies                                      0.018822              0.018428   \n",
       "booking_changes                             0.023118              0.061721   \n",
       "days_in_waiting_list                       -0.065167              0.036037   \n",
       "required_car_parking_spaces                -0.015870             -0.026988   \n",
       "total_of_special_requests                   0.083682              0.090359   \n",
       "total_nights                                0.644935              0.876515   \n",
       "\n",
       "                               adults  children    babies  booking_changes  \\\n",
       "is_canceled                  0.045153 -0.017746 -0.029589        -0.142935   \n",
       "lead_time                    0.119617 -0.056359 -0.024649        -0.007191   \n",
       "arrival_date_day_of_month    0.006034  0.018558 -0.000482         0.022560   \n",
       "stays_in_weekend_nights      0.116679  0.026224  0.018822         0.023118   \n",
       "stays_in_week_nights         0.119901  0.023590  0.018428         0.061721   \n",
       "adults                       1.000000 -0.003216  0.019756        -0.056396   \n",
       "children                    -0.003216  1.000000  0.018410         0.053835   \n",
       "babies                       0.019756  0.018410  1.000000         0.076910   \n",
       "booking_changes             -0.056396  0.053835  0.076910         1.000000   \n",
       "days_in_waiting_list        -0.013475 -0.038615 -0.013391        -0.011225   \n",
       "required_car_parking_spaces  0.013321  0.047881  0.030264         0.059084   \n",
       "total_of_special_requests    0.171793  0.112699  0.098973         0.043884   \n",
       "total_nights                 0.150766  0.031268  0.023608         0.059468   \n",
       "\n",
       "                             days_in_waiting_list  \\\n",
       "is_canceled                              0.075848   \n",
       "lead_time                                0.209741   \n",
       "arrival_date_day_of_month                0.017070   \n",
       "stays_in_weekend_nights                 -0.065167   \n",
       "stays_in_week_nights                     0.036037   \n",
       "adults                                  -0.013475   \n",
       "children                                -0.038615   \n",
       "babies                                  -0.013391   \n",
       "booking_changes                         -0.011225   \n",
       "days_in_waiting_list                     1.000000   \n",
       "required_car_parking_spaces             -0.044083   \n",
       "total_of_special_requests               -0.106080   \n",
       "total_nights                            -0.003906   \n",
       "\n",
       "                             required_car_parking_spaces  \\\n",
       "is_canceled                                    -0.186486   \n",
       "lead_time                                      -0.116867   \n",
       "arrival_date_day_of_month                       0.003816   \n",
       "stays_in_weekend_nights                        -0.015870   \n",
       "stays_in_week_nights                           -0.026988   \n",
       "adults                                          0.013321   \n",
       "children                                        0.047881   \n",
       "babies                                          0.030264   \n",
       "booking_changes                                 0.059084   \n",
       "days_in_waiting_list                           -0.044083   \n",
       "required_car_parking_spaces                     1.000000   \n",
       "total_of_special_requests                       0.087009   \n",
       "total_nights                                   -0.028832   \n",
       "\n",
       "                             total_of_special_requests  total_nights  \n",
       "is_canceled                                  -0.257061      0.001542  \n",
       "lead_time                                    -0.147738      0.157671  \n",
       "arrival_date_day_of_month                     0.005875      0.001898  \n",
       "stays_in_weekend_nights                       0.083682      0.644935  \n",
       "stays_in_week_nights                          0.090359      0.876515  \n",
       "adults                                        0.171793      0.150766  \n",
       "children                                      0.112699      0.031268  \n",
       "babies                                        0.098973      0.023608  \n",
       "booking_changes                               0.043884      0.059468  \n",
       "days_in_waiting_list                         -0.106080     -0.003906  \n",
       "required_car_parking_spaces                   0.087009     -0.028832  \n",
       "total_of_special_requests                     1.000000      0.111533  \n",
       "total_nights                                  0.111533      1.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[features1].corr()\n",
    "df_train[features2].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сохраняем выбранные признаки в переменную для создания графика."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_graph = df_train[features1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создаём график с отфильтрованными данными (без бинарных значений кроме целевого) df_train_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1584x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# корреляция по df_train_graph\n",
    "\n",
    "# корреляция между разными значениями\n",
    "corr = df_train_graph.corr(method='spearman')\n",
    "\n",
    "# установка matplotlib plot конфигурации\n",
    "f, ax = plt.subplots(figsize=(22, 10))\n",
    "\n",
    "# задаём схему графика\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "\n",
    "# устанавливаем цвет\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "\n",
    "# выводим график\n",
    "sns.heatmap(corr, annot=True, mask = mask, cmap=cmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проводим ИАД на тренировочных данных. Посмотрим, есть корреляция между значениями в таблице и целевым значением 'is_calceled'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Вывод:` - мы видим что у целевого признака '`is_canceled`' нет сколько нибудь значимой корреляции с другими параметрами."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выведем, для ознакомления, значения параметра 'reserved_room_type' для последующего создания новых параметров: 'night_cost' и 'one_time_cleaning'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A               ', 'C               ', 'D               ',\n",
       "       'E               ', 'G               ', 'F               ',\n",
       "       'B               '], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_unique = df_train.loc[:, 'reserved_room_type']\n",
    "df_train_unique.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы видим что типы номеров записаны с пробелами. Учтём при создании новых параметров."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создаём две функции: `night` где подсчитываем cтоимось ночного проживания в номере в зависимости от категории номера. И `cleaning` где записываем стоимость затран на уборку номера."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# создаём функцию night\n",
    "\n",
    "def night(row):\n",
    "    row = row['reserved_room_type']\n",
    "    if row == 'A               ':\n",
    "        return 1000\n",
    "    if row == 'B               ':\n",
    "        return 800\n",
    "    if row == 'C               ':\n",
    "        return 600\n",
    "    if row == 'D               ':\n",
    "        return 550\n",
    "    if row == 'E               ':\n",
    "        return 500\n",
    "    if row == 'F               ':\n",
    "        return 450\n",
    "    if row == 'G               ':\n",
    "        return 350"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# создаём функцию night cleaning\n",
    "\n",
    "def cleaning(row):\n",
    "    row = row['reserved_room_type']\n",
    "   \n",
    "    if row == 'A               ':\n",
    "        return 400\n",
    "    if row == 'B               ':\n",
    "        return 350\n",
    "    if row == 'C               ':\n",
    "        return 350\n",
    "    if row == 'D               ':\n",
    "        return 150\n",
    "    if row == 'E               ':\n",
    "        return 150\n",
    "    if row == 'F               ':\n",
    "        return 150\n",
    "    if row == 'G               ':\n",
    "        return 150\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Применяем функции для создания новых колонок со стоимостью номера за ночь и стоимость уборки номера."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# добавляем новые параметры в train\n",
    "df_train['winter_night_cost'] = df_train.apply(night, axis=1)\n",
    "df_train['one_time_cleaning_winter'] = df_train.apply(cleaning, axis=1)\n",
    "\n",
    "# добавляем новые параметры в test\n",
    "df_test['winter_night_cost'] = df_train.apply(night, axis=1)\n",
    "df_test['one_time_cleaning_winter'] = df_train.apply(cleaning, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверяем создание новых параметров и выводим таблицу."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>babies</th>\n",
       "      <th>meal</th>\n",
       "      <th>distribution_channel</th>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <th>previous_cancellations</th>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <th>reserved_room_type</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>total_nights</th>\n",
       "      <th>winter_night_cost</th>\n",
       "      <th>one_time_cleaning_winter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>600</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>FB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>600</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0            0          7               2015               July   \n",
       "1            0         14               2015               July   \n",
       "2            0          0               2015               July   \n",
       "3            0          9               2015               July   \n",
       "4            1         85               2015               July   \n",
       "\n",
       "   arrival_date_day_of_month  stays_in_weekend_nights  stays_in_week_nights  \\\n",
       "0                          1                        0                     1   \n",
       "1                          1                        0                     2   \n",
       "2                          1                        0                     2   \n",
       "3                          1                        0                     2   \n",
       "4                          1                        0                     3   \n",
       "\n",
       "   adults  children  babies       meal distribution_channel  \\\n",
       "0       1         0       0  BB                      Direct   \n",
       "1       2         0       0  BB                       TA/TO   \n",
       "2       2         0       0  BB                      Direct   \n",
       "3       2         0       0  FB                      Direct   \n",
       "4       2         0       0  BB                       TA/TO   \n",
       "\n",
       "   is_repeated_guest  previous_cancellations  previous_bookings_not_canceled  \\\n",
       "0                  0                       0                               0   \n",
       "1                  0                       0                               0   \n",
       "2                  0                       0                               0   \n",
       "3                  0                       0                               0   \n",
       "4                  0                       0                               0   \n",
       "\n",
       "  reserved_room_type  booking_changes  days_in_waiting_list customer_type  \\\n",
       "0   A                               0                     0     Transient   \n",
       "1   A                               0                     0     Transient   \n",
       "2   C                               0                     0     Transient   \n",
       "3   C                               0                     0     Transient   \n",
       "4   A                               0                     0     Transient   \n",
       "\n",
       "   required_car_parking_spaces  total_of_special_requests  total_nights  \\\n",
       "0                            0                          0             1   \n",
       "1                            0                          1             2   \n",
       "2                            0                          0             2   \n",
       "3                            0                          1             2   \n",
       "4                            0                          1             3   \n",
       "\n",
       "   winter_night_cost  one_time_cleaning_winter  \n",
       "0               1000                       400  \n",
       "1               1000                       400  \n",
       "2                600                       350  \n",
       "3                600                       350  \n",
       "4               1000                       400  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
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       "      <th>adults</th>\n",
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       "      <th>customer_type</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>2017</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
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       "      <td>7</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0            1         74               2017            January   \n",
       "1            1         62               2017            January   \n",
       "2            1         62               2017            January   \n",
       "3            1         71               2017            January   \n",
       "4            1        172               2017            January   \n",
       "\n",
       "   arrival_date_day_of_month  stays_in_weekend_nights  stays_in_week_nights  \\\n",
       "0                          1                        1                     0   \n",
       "1                          1                        2                     2   \n",
       "2                          1                        2                     2   \n",
       "3                          1                        2                     2   \n",
       "4                          1                        2                     5   \n",
       "\n",
       "   adults  children  babies       meal distribution_channel  \\\n",
       "0       2         0       0  BB                       TA/TO   \n",
       "1       2         0       0  BB                       TA/TO   \n",
       "2       2         0       0  BB                       TA/TO   \n",
       "3       1         0       0  BB                       TA/TO   \n",
       "4       2         0       0  BB                       TA/TO   \n",
       "\n",
       "   is_repeated_guest  previous_cancellations  previous_bookings_not_canceled  \\\n",
       "0                  0                       0                               0   \n",
       "1                  0                       0                               0   \n",
       "2                  0                       0                               0   \n",
       "3                  0                       0                               0   \n",
       "4                  0                       0                               0   \n",
       "\n",
       "  reserved_room_type  booking_changes  days_in_waiting_list customer_type  \\\n",
       "0   A                               0                     0     Transient   \n",
       "1   A                               0                     0     Transient   \n",
       "2   A                               0                     0     Transient   \n",
       "3   A                               0                     0     Transient   \n",
       "4   A                               0                     0     Transient   \n",
       "\n",
       "   required_car_parking_spaces  total_of_special_requests  total_nights  \\\n",
       "0                            0                          0             1   \n",
       "1                            0                          1             4   \n",
       "2                            0                          1             4   \n",
       "3                            0                          1             4   \n",
       "4                            0                          0             7   \n",
       "\n",
       "   winter_night_cost  one_time_cleaning_winter  \n",
       "0               1000                       400  \n",
       "1               1000                       400  \n",
       "2                600                       350  \n",
       "3                600                       350  \n",
       "4               1000                       400  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# выводим df_train\n",
    "display(df_train.head())\n",
    "\n",
    "# выводим df_test\n",
    "display(df_test.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Учитываем сезонный коэффицент и создаём коэффиценты для последущего расчёта прибыли"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def season_coeff(row):\n",
    "    row = row['arrival_date_month']\n",
    "    '''для каждого сезона вводим коэффицент нагрузки \n",
    "    на который будет умножаться стоимость номера и уборки'''\n",
    "   \n",
    "    if row == 'December' or row == 'January' or row == 'February':\n",
    "        return 1\n",
    "    if row == 'June' or row == 'July' or row == 'August':\n",
    "        return 1.4\n",
    "    else:\n",
    "        return 1.2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создаём новые параметры пр помощи функции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['season_coeff'] = df_train.apply(season_coeff, axis=1)\n",
    "df_test['season_coeff'] = df_test.apply(season_coeff, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выводим датафреймы чтобы убедится что новые признаки добавлены"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>65225</th>\n",
       "      <td>December</td>\n",
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       "    <tr>\n",
       "      <th>65226</th>\n",
       "      <td>December</td>\n",
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       "      <th>65227</th>\n",
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       "<p>65229 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "      arrival_date_month  season_coeff\n",
       "0                   July           1.4\n",
       "1                   July           1.4\n",
       "2                   July           1.4\n",
       "3                   July           1.4\n",
       "4                   July           1.4\n",
       "...                  ...           ...\n",
       "65224           December           1.0\n",
       "65225           December           1.0\n",
       "65226           December           1.0\n",
       "65227           December           1.0\n",
       "65228           December           1.0\n",
       "\n",
       "[65229 rows x 2 columns]"
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>season_coeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>January</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>January</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>January</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>January</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>January</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32407</th>\n",
       "      <td>August</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32408</th>\n",
       "      <td>August</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32409</th>\n",
       "      <td>August</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32410</th>\n",
       "      <td>August</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32411</th>\n",
       "      <td>August</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32412 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      arrival_date_month  season_coeff\n",
       "0                January           1.0\n",
       "1                January           1.0\n",
       "2                January           1.0\n",
       "3                January           1.0\n",
       "4                January           1.0\n",
       "...                  ...           ...\n",
       "32407             August           1.4\n",
       "32408             August           1.4\n",
       "32409             August           1.4\n",
       "32410             August           1.4\n",
       "32411             August           1.4\n",
       "\n",
       "[32412 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_train.loc[:, ['arrival_date_month', 'season_coeff']])\n",
    "display(df_test.loc[:, ['arrival_date_month', 'season_coeff']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выводим общую гистограмму по всем параметрам df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1440 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train.hist(figsize=(15, 20))\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выборочно проверяем некоторые параметры на выбросы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# количество дней между датой бронирования и датой прибытия\n",
    "df_train.boxplot(column=['lead_time'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# количество специальных отметок\n",
    "df_train.boxplot(column=['total_of_special_requests'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выборочно проверяем взаимосвязь целевого и некоторых нецелевых параметров"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Месяц заезда в гостинницу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Заказы (кол-во)')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df_train['arrival_date_month'], df_train['is_canceled']).plot(kind = 'bar')\n",
    "plt.title('Месяц заезда')\n",
    "plt.xlabel('Месяцы')\n",
    "plt.ylabel('Заказы (кол-во)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "День заезда"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Заказы (кол-во)')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df_train['arrival_date_day_of_month'], df_train['is_canceled']).plot(kind = 'bar')\n",
    "plt.title('День заезда')\n",
    "plt.xlabel('День')\n",
    "plt.ylabel('Заказы (кол-во)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заказы по годам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Заказы (кол-во)')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eVtpcCVxZJn5vuXYR8QxvdoeZmVkn8xXrZmZWmIuImZkV5iJiZpZk5+t0be39G7iImJkBvXr1YsOGDV26kEQEGzZsoFevXrnbVPLsLDOzmlFfX09DQwNd/YLkXr16UV9fn3t9FxEzM6BHjx4MGTKk2mnUHHdnmZlZYS4iZmZWmIuImZkV5iJiZmaFuYiYmVlhLiJmZlaYi4iZmRXmImJmZoW5iJiZWWEuImZmVpiLiJmZFeYiYmZmhbmImJlZYW3exVdSPTAN+GvgQOAVYBlwD/CfEfF6RTM0M7PdVqtFRNK/AgOBu4GrgeeBXsC7gcnAlyRdGBGLKp2omZntfto6Erk2IpaViS8D7pS0N3BQx6dlZma1oNUiUlpAUsF4d3q7IiK2RcRrwKoK5mdmZruxXDMbSpoIzAGeAwQMkjTD3VhmZl1b3ulxrwVOiIgVAJLeDdwGHFmpxMzMbPeX9xTfHk0FBCAifgf0qExKZmZWK/IWkSWSfihpYnr8C7CktQaSBkn6paSnJT0l6bwU7ydpgaSV6blvikvS9ZJWSVoqaXTJtmak9VdKmlESP1LSk6nN9ZLU/j+BmZkVlbeIfBp4GvhcejydYq3ZDnw+IkYA44FzJI0ALgR+HhHDgJ+n9wAnAsPS42zg+5AVHeAy4ChgHHBZU+FJ65xV0m5yzs9jZmYdINeYSES8CnwD+EY6S6tnirXWZh2wLr3+k6TlZNecTAUmptXmAAuBL6b4TRERwEOS9pN0QFp3QURsBJC0AJgsaSGwb0Q8lOI3AScD/5nnM5mZ2a7LdSQi6R8lLZE0HfgdsFLSF/LuRNJgYBTwMDAgFRiAPwID0uuBwJqSZg0p1lq8oUzczMw6Sd6zs84hu/XJL4DBwFayMZF/aquhpN7AHcA/RMRLpcMWERGSop05t5uks8m6yDjoIF8baWbWUfKOibwUEUuA/4mIjRHxMlkhaZWkHmQF5JaIuDOF16duKtLz8ym+FhhU0rw+xVqL15eJ7yQiZkXEmIgYU1dX11baZmaWU94icrCkecAQSfMk/RQY0lqDdKbUDcDyiPhGyaJ5QNMZVjOAu0ri09NZWuOBzanbaz5wgqS+aUD9BGB+WvaSpPFpX9NLtmVmZp0gb3fW1PR8bUnsmjba/BXwCeBJSU+k2MXAVcDtks4AVgMfS8vuBaaQ3UblZeCTABGxUdJXgUfSel9pGmQHPgPcCLyNbEDdg+pmZp0o79lZDzS9lvTBiLg7R5tfkd0ipZzjy6wfZGMv5bY1G5hdJr4EOKytXMzMrDKKTEr1lQ7PwszMalKRIuKrws3MDChWRD7V4VmYmVlNynsr+PObvX8fQLOzrszMrIvJeyTyBWCfMg8zM+vC8p7iuy4irqhoJmZmVnPyFpGDJf0H2VXqfwD+KyLuqFhWZmZWE9pzsWE3sov6DgTOlDQhIs6rWGZmZrbba/fFhgCSZgM3VSQjMzOrGXmPRJA0ABib3i6OiI9XJiUzM6sVeecT+RiwGPgo2b2uHpb0kUomZmZmu7+8RyJfAsZGxPMAkuqAnwE/rlRiZma2+8t7ncheTQUk2dCOtmZmtofKeyRyn6T5wG3p/an4tutmZl1e3rOzviDpFOB9KTQrIn5SubTMzKwW5L131j5pets7S2KTI+K+imVmZma7vbzdWfdLmhoRz0vqD1wP9ANcRMxs93R5n2pnkM/lm6udwS7JOzh+ITBf0nnAr4D7IuLEyqVlZma1IPcV65I+QTYP+mfyTI9rZmZ7vrxjIj8FAmgE5kr6BUBEnFTB3MzMbDeXd0zkmopmYWZmNanVIiJJkXmgrXU6PjUzM9vdtTWw/ktJn5V0UGlQ0t6SjpM0B5hRufTMzGx31lZ31mRgJnCbpCHAJrI5RfYC7ge+GRGPVzRDMzPbbbVaRCJiK/A94HuSegD7A69ExKZOyM3MzHZzuW+iGBHbImJd3gIiabak5yUtK4ldLmmtpCfSY0rJsoskrZK0QtKkkvjkFFsl6cKS+BBJD6f4jyTtnfezmJlZx6jknXhvJOsOa+66iBiZHvcCSBoBTAMOTW2+J6mbpG7Ad4ETgRHAaWldgKvTtoYCLwJnVPCzmJlZGRUrIhGxCNiYc/WpwNyIeDUingVWAePSY1VEPBMRrwFzgamSBBzHm/OZzAFO7sj8zcysbdWYE+RcSUtTd1ffFBsIrClZpyHFWor3BzZFxPZmcTMz60R5p8f9k6SXmj8X2N/3gXcBI4F1wLUFttFuks6WtETSksbGxs7YpZlZl5CriETEPhGxb0TsA4wBxkfEvu3dWUSsj4gdEfE68C9k3VUAa4FBJavWp1hL8Q3AfpK6N4u3tN9ZETEmIsbU1dW1N20zM2tBu7qzJF0E/BS4WdJ17d2ZpANK3n4YaDpzax4wTVLPdD3KMGAx8AgwLJ2JtTfZ4Pu8dIX8L4GPpPYzgLvam4+Zme2avPfOavJRsq6orWQ/8C2SdBswEdhfUgNwGTBR0kiymzk+B3wKICKeknQ78DSwHTgnInak7ZwLzAe6AbMj4qm0iy+S3Qzya8DjwA3t/CxmZraL2ltEiIhXACS93MZ6p5UJt/hDHxFXAleWid9Ldgv65vFneLM7zMzMqiDvreCfJDt6GCppKSBgcAXzMjOzGpD3SOSDFc3CzMxqUt6B9fXAu9PzgWRjHc9XKCczM6sReY9EbiO7wG8b0DSr/BTg1EokZWZmtSFvETkYGAX8EXhnij3V8upmZtYV5O3O2pYuEPx2RLyeXr9awbzMzKwG5C0i3waIiK8CSOpDmdNuzcysa8nVnRURc5q93wxcXJGMzMysZuS9TqSO7ArxEUCvpnhEHFehvMzMrAbk7c66BVgODAGuILtlSau3PTEzsz1f3iLSPyJuIBtgfyAiZpJNCmVmZl1Y3lN8t6XndZI+APwB6FeZlMzMrFbkLSJfS2dkfZ7sTK19gX+sWFZmZlYT8p6ddXd6uRk4FkBSr5ZbmJlZV5B3etxLm71/Px5YNzPr8vIOrL9T0vcl7S9pDnABMLWCeZmZWQ3IO8f6Z8gG09cAv4mIE9KkUGZm1oXlvdjwFLIbLv4M+DtJzwNExJ0VzM3MzHZzec/O+lB6fiE9PkQ206GLiJlZF5b37KxPVjoRMzOrPXm7s3oBZwCH8tZ7Z82sUF5mZlYD8p6ddTPZZFSTgAeAeuBPlUrKzMxqQ94xkaER8VFJUyNijqRbgQcrmVhXN/jCe6qdQpueu+oD1U7BzKos98yG6XmTpMOAPsA7KpOSmZnVirxHIrMk9QUuAeYBvdNrMzPrwvKenfXD9PIB4ODKpWNmZrWk1e4sSb0kXSjp7yV1k3SZpJ9K+rKkVguQpNmSnpe0rCTWT9ICSSvTc98Ul6TrJa2StFTS6JI2M9L6KyXNKIkfKenJ1OZ6SSr+ZzAzsyLaGhP5NtnYx+FkRyHvAP4J2C89t+ZGYHKz2IXAzyNiGPDz9B7gRGBYepwNfB+yogNcBhwFjAMuayo8aZ2zSto135eZmVVYW91ZR0bEaEl7AeuBCRHxuqQHgUdbaxgRiyQNbhaeCkxMr+cAC8nmbp8K3BQRATwkaT9JB6R1F0TERgBJC4DJkhYC+0bEQyl+E3Ay8J9tfWAzM+s4bR2JbAOIiNeBhvRM+rEvYkBErEuv/wgMSK8Hkt3csUlDirUWbygTNzOzTtTmKb6S9k0vjy6JDeLN034LSYWoaDFqF0lnS1oiaUljY2Nn7NLMrEtoq4hMJ/3QR8TWknhP4FMF9rc+dVORnp9P8bXAoJL16lOstXh9mXhZETErIsZExJi6uroCaZuZWTmtFpGIWBERO93eJCJWRcQTBfY3D2g6w2oGcFdJfHo6S2s8sDl1e80HTpDUNw2onwDMT8tekjQ+nZU1vWRbZmbWSfJebNhukm4jGxjfX1ID2VlWVwG3SzoDWA18LK1+LzAFWAW8DHwSICI2Svoqb07F+5WmQXbgM2RngL2NbEDdg+pmZp2sYkUkIk5rYdHxZdYN4JwWtjMbmF0mvgQ4bFdyNDOzXZP33llmZmY7cRExM7PCXETMzKwwFxEzMyvMRcTMzApzETEzs8JcRMzMrDAXETMzK8xFxMzMCnMRMTOzwlxEzMysMBcRMzMrzEXEzMwKcxExM7PCKnYreOsCLu9T7QzyuXxztTMw22P5SMTMzApzETEzs8JcRMzMrDAXETMzK8xFxMzMCnMRMTOzwlxEzMysMBcRMzMrzEXEzMwKcxExM7PCqlJEJD0n6UlJT0hakmL9JC2QtDI9901xSbpe0ipJSyWNLtnOjLT+SkkzqvFZzMy6smoeiRwbESMjYkx6fyHw84gYBvw8vQc4ERiWHmcD34es6ACXAUcB44DLmgqPmZl1jt2pO2sqMCe9ngOcXBK/KTIPAftJOgCYBCyIiI0R8SKwAJjcyTmbmXVp1SoiAdwv6VFJZ6fYgIhYl17/ERiQXg8E1pS0bUixluJmZtZJqnUr+PdFxFpJ7wAWSPpt6cKICEnRUTtLhepsgIMOOqijNmtm1uVV5UgkItam5+eBn5CNaaxP3VSk5+fT6muBQSXN61OspXi5/c2KiDERMaaurq4jP4qZWZfW6UVE0l9I2qfpNXACsAyYBzSdYTUDuCu9ngdMT2dpjQc2p26v+cAJkvqmAfUTUszMzDpJNbqzBgA/kdS0/1sj4j5JjwC3SzoDWA18LK1/LzAFWAW8DHwSICI2Svoq8Eha7ysRsbHzPoaZmXV6EYmIZ4AjysQ3AMeXiQdwTgvbmg3M7ugczcwsn93pFF8zM6sxLiJmZlaYi4iZmRXmImJmZoW5iJiZWWEuImZmVpiLiJmZFeYiYmZmhbmImJlZYS4iZmZWmIuImZkV5iJiZmaFuYiYmVlhLiJmZlaYi4iZmRXmImJmZoW5iJiZWWEuImZmVpiLiJmZFeYiYmZmhbmImJlZYS4iZmZWmIuImZkV5iJiZmaFuYiYmVlhLiJmZlZYzRcRSZMlrZC0StKF1c7HzKwrqekiIqkb8F3gRGAEcJqkEdXNysys66jpIgKMA1ZFxDMR8RowF5ha5ZzMzLqM7tVOYBcNBNaUvG8Ajmq+kqSzgbPT2y2SVnRCbns8wf7AC9XOo01XqNoZWBX4+9nh/rJcsNaLSC4RMQuYVe089jSSlkTEmGrnYVaOv5+do9a7s9YCg0re16eYmZl1glovIo8AwyQNkbQ3MA2YV+WczMy6jJruzoqI7ZLOBeYD3YDZEfFUldPqStxFaLszfz87gSKi2jmYmVmNqvXuLDMzqyIXETMzK8xFxMzMCnMRMTOzwlxErBBJ75N0vqQTqp2LmaSjJO2bXr9N0hWSfirpakl9qp3fnsxFxHKRtLjk9VnAd4B9gMt892TbDcwGXk6vvwX0Aa5OsX+tVlJdgU/xtVwkPR4Ro9LrR4ApEdEo6S+AhyLivdXN0LoyScsjYnh6/VhEjC5Z9kREjKxacns4H4lYXntJ6iupP9l/PhoBIuLPwPbqpmbGMkmfTK//W9IYAEnvBrZVL609X01fsW6dqg/wKCAgJB0QEesk9U4xs2o6E/iWpC+T3bn3N5LWkN3l+8yqZraHc3eW7RJJbwcGRMSz1c7FLA2uDyH7D3JDRKyvckp7PBcR22WSekfElmrnYVaOv5+V5TER6whPVzsBs1b4+1lBHhOxXCSd39IioHdn5mLWnL+f1eMjEcvr60BfsmtDSh+98ffIqs/fzyrxkYjl9RjwHxHxaPMFknz2i1Wbv59V4oF1y0XSIcDGputDmi0b4LNgrJr8/aweFxEzMyvMfYWWi6Q+kq6S9FtJGyVtkLQ8xfardn7Wtfn7WT0uIpbX7cCLwMSI6BcR/YFjU+z2qmZm5u9n1bg7y3KRtCIiDmnvMrPO4O9n9fhIxPJaLekCSQOaApIGSPoi2f2JzKrJ388qcRGxvE4F+gMPSHpR0kZgIdAP+Fg1EzPD38+qcXeW5SbpPUA92fwhW0rikyPivuplZubvZ7X4SMRykfQ54C7gXLK5G6aWLP56dbIyy/j7WT2+Yt3yOgs4MiK2SBoM/FjS4Ij4Fp5PxKrP388qcRGxvPZq6iKIiOckTST7h/qX+B+pVZ+/n1Xi7izLa72kkU1v0j/YDwL7A55f3arN388q8cC65SKpHtgeEX8ss+yvIuK/qpCWGeDvZzW5iJiZWWHuzjIzs8JcRMzMrDCfnWXWSSTtAJ4sCf1zRPxztfIx6wgeEzHrJJK2RITn+7Y9iruzzKpM0mmSnpS0TNLVzZZtkfSEpKclLatWjmYtcRExqyJJBwJXA8cBI4Gxkk4uXSUiRgJTOj05sxxcRMyqayywMCIaI2I7cAswAUBSd+DlaiZn1hYXEbPd12BgbbWTMGuNi4hZdS0G/kbS/pK6AacBD6RlHwXurlpmZjn47CyzTtLS2VmSTgMuJrtR4D0R8UVJJwF3Ar8HXgL2BoYA/+jTgm134iJithuSdDpARNxYEjsM+EhEXF6drMx25iJithuSNAQgIp4tifUBBkWET/W13YaLiJmZFeaBdTMzK8xFxMzMCnMRMTOzwlxEzMysMBcRMzMrzEXEzMwK+19W9ckaiJ4uLAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df_train['arrival_date_year'], df_train['is_canceled']).plot(kind = 'bar')\n",
    "plt.title('Заказы по годам')\n",
    "plt.xlabel('Год')\n",
    "plt.ylabel('Заказы (кол-во)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Количество младенцев"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Заказы (кол-во)')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df_train['babies'], df_train['is_canceled']).plot(kind = 'bar')\n",
    "plt.title('Количество младенцев')\n",
    "plt.xlabel('Младенцы')\n",
    "plt.ylabel('Заказы (кол-во)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тип заказчика"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Заказы (кол-во)')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df_train['customer_type'], df_train['is_canceled']).plot(kind = 'bar')\n",
    "plt.title('Тип заказчика')\n",
    "plt.xlabel('Тип заказа')\n",
    "plt.ylabel('Заказы (кол-во)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>is_canceled</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>1656</td>\n",
       "      <td>1090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <td>263</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transient</th>\n",
       "      <td>27476</td>\n",
       "      <td>18017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transient-Party</th>\n",
       "      <td>11790</td>\n",
       "      <td>4913</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "is_canceled          0      1\n",
       "customer_type                \n",
       "Contract          1656   1090\n",
       "Group              263     24\n",
       "Transient        27476  18017\n",
       "Transient-Party  11790   4913"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_train['customer_type'], df_train['is_canceled'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Канал дистрибуции заказа"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Заказы (кол-во)')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df_train['distribution_channel'], df_train['is_canceled']).plot(kind = 'bar')\n",
    "plt.title('Канал дистрибуции заказа')\n",
    "plt.xlabel('Тип заказа')\n",
    "plt.ylabel('Заказы (кол-во)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>is_canceled</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distribution_channel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Corporate</th>\n",
       "      <td>2992</td>\n",
       "      <td>831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Direct</th>\n",
       "      <td>5665</td>\n",
       "      <td>1188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDS</th>\n",
       "      <td>80</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TA/TO</th>\n",
       "      <td>32448</td>\n",
       "      <td>22006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "is_canceled               0      1\n",
       "distribution_channel              \n",
       "Corporate              2992    831\n",
       "Direct                 5665   1188\n",
       "GDS                      80     19\n",
       "TA/TO                 32448  22006"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_train['distribution_channel'], df_train['is_canceled'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Вывод:` наибольшая пропорция отмены бронирования через канал `TA/TO`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Опции заказа"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Заказы (кол-во)')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df_train['meal'], df_train['is_canceled']).plot(kind = 'bar')\n",
    "plt.title('Опции заказа')\n",
    "plt.xlabel('Опция')\n",
    "plt.ylabel('Заказы (кол-во)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тип забронированной комнаты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Заказы (кол-во)')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N4CJiZmaFuYiYmVlhLiJmZlaYi4iZmRXmImJmZoW5iJiZ1ajJkyezww47sOeee1YtB39PxMysLZy3bRvvb12Lqxx//PGccsopTJw4sW2P3QpuiZiZ1agxY8bQq1evqubgImJmZoW5iJiZWWEuImZmVljFioik/pLulvSEpMclnZ7i50laIWl+eh2W2+ZMSUslLZY0Nhcfl2JLJU3LxQdKeiDFr5O0RaXOx8zM3q+SLZENwL9GxBBgNDBVUsPT438UEcPS61aAtGwCsAcwDrhUUhdJXYCfAIcCQ4Bjc/v5QdrXIOBF4MQKno+ZWYdy7LHHst9++7F48WLq6uq4/PLL2z2Hig3xjYiVwMo0/bKkRUBzj9waD8yKiDeBpyQtBUalZUsj4kkASbOA8Wl/BwGfT+tcCZwHXNbW52Jm1qIyhuS2tWuvvbbdj9lYu1wTkTQAGA48kEKnSFogaaakninWD1ie26w+xZqK9wbWRsSGRvFSx58iaZ6keatWrWqLUzIzM9qhiEjaCrgB+GpEvETWUvgoMIyspXJRpXOIiBkRMSIiRvTp06fShzMz6zQq+o11Sd3ICsjVEXEjQEQ8l1v+M+CWNLsC6J/bvC7FaCK+GthOUtfUGsmvb2Zm7aCSo7MEXA4siogf5uI75lY7EliYpmcDEyR1lzQQGAw8CDwEDE4jsbYgu/g+OyICuBv4bNp+EnBTpc7HzDZ/2a+Vzq21n0ElWyKfAI4DHpM0P8XOIhtdNQwI4GngSwAR8bik64EnyEZ2TY2IjQCSTgFuB7oAMyPi8bS/fwdmSTofeJSsaJmZtVqPHj1YvXo1vXv3JvsbuPOJCFavXk2PHj3K3qaSo7PuA0r9S9zazDYXABeUiN9aars0YmtU47iZWWvV1dVRX19PZx9806NHD+rq6spe33fxNTMDunXrxsCBA6udRs3xbU/MzKwwFxEzMyvMRcTMzApzETEzs8JcRMzMrDAXETMzK8xFxMzMCnMRMTOzwlxEzMysMBcRMzMrzEXEzMwKcxExM7PCXETMzKywFu/iK6mO7EFQnwJ2Al4ne5DU74DfR8TbFc3QzMw6rGaLiKT/AfqRPcL2B8DzQA9gV2Ac8E1J0yJibqUTNTOzjqellshFEbGwRHwhcGN6XO3ObZ+WmZnVgmaLSL6ApIKxa5pdHBHrI+ItYGkF8zMzsw6srCcbSjoAuJLsmegC+kua5G4sM7POrdzH414EHBIRiwEk7QpcC+xTqcTMzKzjK3eIb7eGAgIQEX8FulUmJTMzqxXltkTmSfo58Ms0/wVgXmVSMjOzWlFuEfkyMBU4Lc3fC1xakYzMzKxmlFVEIuJN4IfAD9More4pZmZmnVhZ10QkfU3SPEkTgb8CSyR9o4Vt+ku6W9ITkh6XdHqK95I0R9KS9N4zxSXpx5KWSlogae/cvial9ZdImpSL7yPpsbTNjyWpyIdgZmbFlHthfSrwL8AlwN7ALsAJLWyzAfjXiBgCjAamShoCTAPuiojBwF1pHuBQYHB6TQEug6zoAOcC+wKjgHMbCk9a5+TcduPKPB8zM2sD5RaRlyJiHvC3iFgTEa8BbzS3QUSsjIhH0vTLwCKyW6iMJ/vOCen9iDQ9HrgqMvcD20naERgLzEnHfRGYA4xLy7aJiPsjIoCrcvsyM7N2UO6F9V0kzQYGpncBA8s9iKQBwHDgAaBvRKxMi54F+qbpfsDy3Gb1KdZcvL5E3MzM2km5RWR8er8oF5tezoaStgJuAL4aES/lL1tEREiKMnMoTNIUsi4ydt7Zt/oyM2sr5Y7O+mPDtKR/iohbytlOUjeyAnJ1RNyYws9J2jEiVqYuqedTfAXQP7d5XYqtAA5oFL8nxetKrF8q/xnADIARI0ZUtmidt20r119XmTzMzNpBkYdSfaecldJIqcuBRRHxw9yi2UDDCKtJwE25+MQ0Sms0sC51e90OHCKpZ7qgfghwe1r2kqTR6VgTc/syM7N2UG53Vl65w2g/ARwHPCZpfoqdBVwIXC/pRGAZcHRaditwGNldgV8jjf6KiDWSvgs8lNb7TkSsSdNfAa4AtgR+n15mZtZOihSRL5WzUkTcR9MF5+AS6wfZUOJS+5oJzCwRnwfsWU4+ZmbW9sq9FfzXG81/EqBRN5WZmXUy5V4T+QawdYmXmZl1YuV2Z62MiG9XNBMzM6s5rfmy4W/JvqX+d+B/I+KGimVlZmY1oTVfNuxCNgpqJ+AkSWMi4vSKZWZmZh1eq79sCCBpJtm9qszMrBMre4ivpL7AyDT7YER8oTIpmZlZrSj3eSJHAw8CnyP7cuADkj5bycTMzKzjK7cl8k1gZEQ8DyCpD3An8OtKJWZmZh1fud8T+UBDAUlWt2JbMzPbTJXbErlN0u3AtWn+GHyfKjOzTq/c0VnfkHQU8MkUmhERv6lcWmZmVgvKvXfW1ul5IDfmYuMi4raKZWZmZh1eudc17pC0A4Ck3pKuBvxFQzOzTq7cIjINuF3S6cB9wG0RcWjl0jIzs1pQ9jfWJR1H9uCor5T7eFwzM9u8lXtN5GYggFXALEl/AIiIwyuYm5mZdXDlDvGdXtEszMysJjVbRCQpMn9saZ22T83MzDq6li6s3y3pVEk754OStpB0kKQrgUmVS8/MzDqylrqzxgGTgWslDQTWkj1T5APAHcB/RcSjFc3QzMw6rGaLSES8AVwKXCqpG7A98HpErG2H3MzMrIMr+3kiEbEeWFnBXMzMrMb4TrxmZlaYi4iZmRVWsSIiaaak5yUtzMXOk7RC0vz0Oiy37ExJSyUtljQ2Fx+XYkslTcvFB0p6IMWvk7RFpc7FzMxKK/fxuC9LeqnxewubXUE2uquxH0XEsPS6Ne1/CDAB2CNtc6mkLpK6AD8BDgWGAMemdQF+kPY1CHgROLGcczEzs7ZTVhGJiK0jYpuI2BoYAYyOiG1a2GYusKbMPMYDsyLizYh4ClgKjEqvpRHxZES8BcwCxksScBDvPp73SuCIMo9lZmZtpFXdWZLOBG4GfiHpRwWPeYqkBam7q2eK9QOW59apT7Gm4r2BtRGxoVG8qbynSJonad6qVasKpm1mZo219prI54BhwD7Apwoc7zLgo2kfK4GLCuyj1SJiRkSMiIgRffr0aY9Dmpl1CmV/T6RBRLwOIOm1Ats+1zAt6WdAwy3lVwD9c6vWpRhNxFcD20nqmloj+fXNzKydlHth/TFJC4CPpa6ox4DhrT2YpB1zs0cCDSO3ZgMTJHVPt1cZDDwIPAQMTiOxtiC7+D473fDxbuCzaftJwE2tzcfMzDZNuS2Rf2rtjiVdCxwAbC+pHjgXOEDSMLJnkzwNfAkgIh6XdD3wBLABmBoRG9N+TgFuB7oAMyPi8XSIfyd7tsn5wKPA5a3N0czMNk25ReQ5smsg95K1QHYFrm9ug4g4tkS4yV/0EXEBcEGJ+K1kT1RsHH+SbPSWmZlVSblF5FqyEVHrgXUpdhhwTCWSMjOz2lBuEdmFrAXyLPDhFHu86dXNzKwzKHeI7/qIeBu4OCLeTtNvVjAvMzOrAeUWkYsBIuK7AJK2pcR1CjMz61zK6s6KiCsbza8DzqpIRmZmVjPKKiKS+pANqR0C9GiIR8RBFcrLzMxqQLndWVcDi4CBwLfJvuPxUIVyMjOzGlFuEekdEZeTXWD/Y0RMJruLrpmZdWLlDvFdn95XSvoM8HegV2VSMjOzWlFuETk/jcj6V7KRWtsAX6tYVmZmVhPKHZ3VcLfddcCBAJJ6NL2FmZl1BuXexfecRvOfxhfWzcw6vXIvrH9Y0mWStpd0JXAG2SNtzcysEyv3GetfIbuYvhz4c0Qcku6ia2ZmnVi5XzY8iuyGi3cCX5T0PEBE3FjB3MzMrIMrd3TWP6f3F9Lrn8keLOUiYmbWiZU7OuuESidiZma1p9zurB7AicAevPfeWZMrlJeZmdWAckdn/YLsYVRjgT8CdcDLlUrKzMxqQ7lFZFBEnA28mm4L/xlg38qlZWZmtaDsJxum97WS9gS2BXaoTEpmZlYryh2dNUNST+BsYDawVZo2M7NOrNzRWT9Pk38EdqlcOmZmVkua7c6S1EPSNEn/IqmLpHMl3SzpW5LKbcWYmdlmqqVrIheTXfvYi6wVsgPwn8B26b1JkmZKel7Swlysl6Q5kpak954pLkk/lrRU0gJJe+e2mZTWXyJpUi6+j6TH0jY/lqRWnruZmW2ilorIPhHxdeAUYDfg1IiYC3wD+IcWtr0CGNcoNg24KyIGA3eleYBDgcHpNQW4DLKiA5xLNhJsFHBuQ+FJ65yc267xsczMrMJaKiLrASLibaA+vRMR0dKOU7FZ0yg8HrgyTV8JHJGLXxWZ+4HtJO1I9r2UORGxJiJeBOYA49KybSLi/pTLVbl9mZlZO2lxiK+kbdLkfrlYf94d9tsafSNiZZp+FuibpvuR3SG4QX2KNRevLxE3M7N21FIRmUh2o0Ui4o1cvDvwpU05cGpBtNiiaQuSpkiaJ2neqlWr2uOQZmadQrNFJCIWR8T7bm8SEUsjYn6B4z2XuqJI78+n+Aqgf269uhRrLl5XIl5SRMyIiBERMaJPnz4F0jYzs1LK/cZ6W5kNNIywmgTclItPTKO0RgPrUrfX7cAhknqmC+qHALenZS9JGp1GZU3M7cvMzNpJxb7rIela4ABge0n1ZKOsLgSul3QisAw4Oq1+K3AYsBR4DTgBICLWSPou7z7P/TsR0XCx/itkI8C2BH6fXmZm1o4qVkQi4tgmFh1cYt0Apjaxn5nAzBLxecCem5KjmZltmvbuzjIzs82Ii4iZmRXmImJmZoW5iJiZWWEuImZmVpiLiJmZFeYiYmZmhbmImJlZYS4iZmZWmIuImZkV5iJiZmaFuYiYmVlhLiJmZlaYi4iZmRXmImJmZoW5iJiZWWEuImZmVpiLiJmZFVaxx+OadXYDpv2uVes/feFnKpSJWeW4JWJmZoW5iJiZWWEuImZmVpiLiJmZFeYiYmZmhbmImJlZYVUpIpKelvSYpPmS5qVYL0lzJC1J7z1TXJJ+LGmppAWS9s7tZ1Jaf4mkSdU4FzOzzqyaLZEDI2JYRIxI89OAuyJiMHBXmgc4FBicXlOAyyArOsC5wL7AKODchsJjZmbtoyN1Z40HrkzTVwJH5OJXReZ+YDtJOwJjgTkRsSYiXgTmAOPaOWczs06tWkUkgDskPSxpSor1jYiVafpZoG+a7gcsz21bn2JNxc3MrJ1U67Ynn4yIFZJ2AOZI+kt+YUSEpGirg6VCNQVg5513bqvdmpl1elVpiUTEivT+PPAbsmsaz6VuKtL782n1FUD/3OZ1KdZUvNTxZkTEiIgY0adPn7Y8FTOzTq3di4ikD0naumEaOARYCMwGGkZYTQJuStOzgYlplNZoYF3q9rodOERSz3RB/ZAUMzOzdlKN7qy+wG8kNRz/moi4TdJDwPWSTgSWAUen9W8FDgOWAq8BJwBExBpJ3wUeSut9JyLWtN9pmJlZuxeRiHgSGFoivho4uEQ8gKlN7GsmMLOtczQzs/J0pCG+ZmZWY1xEzMysMBcRMzMrzEXEzMwKcxExM7PCXETMzKwwFxEzMyvMRcTMzApzETEzs8KqdRdfM2vsvG1buf66yuRh1gpuiZiZWWEuImZmVpiLiJmZFeYiYmZmhbmImJlZYS4iZmZWmIf4mllJA6b9rlXrP33hZyqUiXVkbomYmVlhLiJmZlaYi4iZmRXmImJmZoW5iJiZWWEenWVmmyWPLmsfbomYmVlhbolsxvyXmLUr38q+U6r5IiJpHPDfQBfg5xFxYZVTMjPbZLXyR2BNFxFJXYCfAP8I1AMPSZodEU9UN7Ma5b8krTPzz38hNV1EgFHA0oh4EkDSLGA84CKyGaiVv8TMOoQqFUFFRJvsqBokfRYYFxEnpfnjgH0j4pRG600BpqTZ3YDFFUxre+CFCu6/kmo5d3D+1eb8q6vS+X8kIvo0DtZ6S6QsETEDmNEex5I0LyJGtMex2lot5w7Ov9qcf3VVK/9aH+K7Auifm69LMTMzawe1XkQeAgZLGihpC2ACMLvKOZmZdRo13Z0VERsknQLcTjbEd2ZEPF7ltNql26xCajl3cP7V5vyrqyr51/SFdTMzq65a784yM7MqchExM7PCXETMzKwwFxEzMyvMRcTMzApzETEzs8JcRMzMrDAXETMzK8xFxMzMCnMRMTOzwlxEzMysMBcRMzMrzEXEzMwKcxExM7PCXETMzKwwFxEzMyvMRcTMzApzETEzs8JcRMzMrDAXETMzK8xFxMzMCnMRMTOzwlxEzMysMBcRMzMrzEXEzMwKcxExM7PCXETMzKwwFxEzMyvMRcTMzApzETEzs8JcRMzMrDAXETMzK8xFxMzMCnMRMTOzwlxEzMysMBcRMzMrzEXEzMwKcxExM7PCXETMzKwwFxEzMyvMRcTMzApzETEzs8JcRMzMrDAXETMzK8xFxMzMCnMRMTOzwlxEzMysMBcRMzMrzEXEzMwKcxFpY5I+Kekn1c6jtSRtL0nVzqMckgZJ+kSJ+CckfbQaORUlqY+kPtXOozORtHO1c9icuIi0AUnDJf2npKeB7wJ/qXJKzZI0WtI9km5MuS8EFgLPSRpX7fzK8F/ASyXiL6VlHZoy50l6AVgM/FXSKknnVDu3ckg6Izf9uUbLvtf+GbXabxsmJN1QxTwKkTRe0tTc/AOSnkyvz7Z3Pi4iBUnaVdK5kv4CXAw8AygiDoyIi6ucXksuAb4HXAv8ATgpIj4MjAG+X83EytQ3Ih5rHEyxAe2fTqt9DfgEMDIiekVET2Bf4BOSvlbd1MoyITd9ZqNltfBHSL7FvUvVsijuDGB2br47MBI4APhyeyfjIlLcX4CDgH+KiE+mwrGxyjmVq2tE3BERvwKejYj7ASKiQ7egcrZrZtmW7ZXEJjgOODYinmoIRMSTwBeBiVXLqnxqYrrUfEcUTUzXii0iYnlu/r6IWB0RzwAfau9kXESKOwpYCdwt6WeSDqY2/gMBvJ2bfr3Rslr4TzVP0smNg5JOAh6uQj6t1S0iXmgcjIhVQLcq5NNazf0SroWfn6GSXpL0MrBXmn5J0suSSnWTdjQ98zMRcUputt2vrymiFv7NOy5JHwLGA8eStUyuAn4TEXdUNbFmSNoIvEpW9LYEXmtYBPSIiA79i0xSX+A3wFu8WzRGAFsAR0bEs9XKrRySHomIvVu7rKOo9Z+fWifpauCeiPhZo/iXgAMi4th2zcdFpO1I6gl8DjgmIg6udj6bO0kHAnum2ccj4g/VzKdcuV/C71uEfwlbCyTtQDY44E3gkRTeh+zayBER8Vy75uMiYmZWeyQdBOyRZqv2R5SLiJmZFeYL62ZmVpiLiJmZFda12gmYdQaSegN3pdkPk32naFWaHxURb1UlMbNN5GsiZu1M0nnAKxExvdq5mG0qd2eZVZGkAeneZUjqlu5/dEmav0LSU5Lmp9frkgZUNWGzRtydZdZxTAFeaRT7RkT8GqCh2Jh1JG6JmHUA6c4HJwCXVjsXs9ZwS8SsYzgdmEF2KxezmuGWiFn1bQscAcysch5mreYiYlZ9dcBFEbGh2omYtZaH+JqZWWFuiZiZWWEuImZmVpiLiJmZFeYiYmZmhbmImJlZYS4iZmZWmIuImZkV5iJiZmaF/X92gqAqxk9FSwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df_train['reserved_room_type'], df_train['is_canceled']).plot(kind = 'bar')\n",
    "plt.title('Тип забронированной комнаты')\n",
    "plt.xlabel('Тип')\n",
    "plt.ylabel('Заказы (кол-во)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Количество изменений параметров заказа"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Заказы (кол-во)')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df_train['booking_changes'], df_train['is_canceled']).plot(kind = 'bar')\n",
    "plt.title('Количество изменений параметров заказа')\n",
    "plt.xlabel('изменения')\n",
    "plt.ylabel('Заказы (кол-во)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Количество детей от 3 до 14 лет"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Заказы (кол-во)')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df_train['children'], df_train['is_canceled']).plot(kind = 'bar')\n",
    "plt.title('Количество детей от 3 до 14 лет')\n",
    "plt.xlabel('Дети')\n",
    "plt.ylabel('Заказы (кол-во)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тип заказчика"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Заказы (кол-во)')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df_train['customer_type'], df_train['is_canceled']).plot(kind = 'bar')\n",
    "plt.title('Тип заказчика')\n",
    "plt.xlabel('Тип')\n",
    "plt.ylabel('Заказы (кол-во)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Количество отменённых заказов у клиента"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Заказы (кол-во)')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df_train['previous_cancellations'], df_train['is_canceled']).plot(kind = 'bar')\n",
    "plt.title('Количество отменённых заказов у клиента')\n",
    "plt.xlabel('Количество отмен')\n",
    "plt.ylabel('Заказы (кол-во)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>is_canceled</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous_cancellations</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40943</td>\n",
       "      <td>18648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>135</td>\n",
       "      <td>5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "is_canceled                 0      1\n",
       "previous_cancellations              \n",
       "0                       40943  18648\n",
       "1                         135   5232\n",
       "2                          31     23\n",
       "3                          35     16\n",
       "4                           4      1\n",
       "5                          11      2\n",
       "6                           0      7\n",
       "11                         25     10\n",
       "13                          1     11\n",
       "14                          0     11\n",
       "19                          0     17\n",
       "21                          0      1\n",
       "24                          0     28\n",
       "25                          0     19\n",
       "26                          0     18"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_train['previous_cancellations'], df_train['is_canceled'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Вывод:` те кто отменял заказ хотя бы однажды имеют повышенную вероятность отменить новый заказ. То же можно утверждать про тех кто отменял заказы > 13 раз."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Количество специальных отметок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Заказы (кол-во)')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df_train['total_of_special_requests'], df_train['is_canceled']).plot(kind = 'bar')\n",
    "plt.title('Количество специальных отметок')\n",
    "plt.xlabel('Отметка')\n",
    "plt.ylabel('Заказы (кол-во)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>is_canceled</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21170</td>\n",
       "      <td>19501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13884</td>\n",
       "      <td>3074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5191</td>\n",
       "      <td>1253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>833</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>94</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "is_canceled                    0      1\n",
       "total_of_special_requests              \n",
       "0                          21170  19501\n",
       "1                          13884   3074\n",
       "2                           5191   1253\n",
       "3                            833    203\n",
       "4                             94     13\n",
       "5                             13      0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_train['total_of_special_requests'], df_train['is_canceled'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Вывод:` постояльцы отмеченные специальной меткой `0` имеют более высокие шансы отменить заказ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проводим анализ баланса классов в целевой переменной"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Классы (кол-во)')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df_train['is_canceled'] == 0, df_train['is_canceled'] == 1).plot(kind = 'bar')\n",
    "plt.title('Дизбалланс классов целевого признака')\n",
    "plt.xlabel('Класс')\n",
    "plt.ylabel('Классы (кол-во)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is_canceled = 0:  41185\n",
      "is_canceled = 1:  24044\n"
     ]
    }
   ],
   "source": [
    "print('is_canceled = 0: ',df_train.loc[df_train['is_canceled'] == 0, 'is_canceled'].count())\n",
    "print('is_canceled = 1: ',df_train.loc[df_train['is_canceled'] == 1, 'is_canceled'].count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Вывод:` Количество отменённых бронирований составляет 58% от количества нетменённых бронирований."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Вывод ИАД:` Наблюдаются выбросы в количествах дней между датой бронирования и датой прибытия, что не вызывает подозрений те некоторые киенты могли забронировать номер задолго до заезда. Также есть выбивающиеся значения в параметре 'количество специальных отметок', просто отметим этот факт.\n",
    "\n",
    "Отметим, что наибольшая пропорция отмены бронирования через канал `TA/TO`\n",
    "\n",
    "Те кто `отменял заказ` хотя бы `однажды` имеют повышенную вероятность отменить новый заказ. То же можно утверждать про тех кто отменял заказы > 13 раз.\n",
    "\n",
    "Постояльцы отмееные специальной меткой `0` имеют более высокие шансы отменить заказ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.4. <a id='toc1_1_4_'></a>[Шаг 3. Формулировка ML-задачи на основе бизнес-задачи](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создаём функцию для подсчёта прибыли"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def profit(row):\n",
    "    if row['is_canceled'] == 0:\n",
    "        income = row['winter_night_cost'] * row['season_coeff'] * row['total_nights']\n",
    "        expences = row['one_time_cleaning_winter'] * round(row['total_nights'] / 2)\n",
    "    else:\n",
    "        income = 0\n",
    "        expences = row['winter_night_cost'] * row['season_coeff'] + row['one_time_cleaning_winter']\n",
    "    earned = income - expences\n",
    "    return earned\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создаём новый признак при помощи функции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test['profit'] = df_test.apply(profit, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверяем создание нового признака с прибылью / убытком"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>winter_night_cost</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>-1400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>-1400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600</td>\n",
       "      <td>-950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>600</td>\n",
       "      <td>-950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>-1400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32407</th>\n",
       "      <td>1000</td>\n",
       "      <td>7200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32408</th>\n",
       "      <td>1000</td>\n",
       "      <td>8200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32409</th>\n",
       "      <td>1000</td>\n",
       "      <td>8200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32410</th>\n",
       "      <td>1000</td>\n",
       "      <td>8200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32411</th>\n",
       "      <td>1000</td>\n",
       "      <td>8200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32412 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       winter_night_cost  profit\n",
       "0                   1000 -1400.0\n",
       "1                   1000 -1400.0\n",
       "2                    600  -950.0\n",
       "3                    600  -950.0\n",
       "4                   1000 -1400.0\n",
       "...                  ...     ...\n",
       "32407               1000  7200.0\n",
       "32408               1000  8200.0\n",
       "32409               1000  8200.0\n",
       "32410               1000  8200.0\n",
       "32411               1000  8200.0\n",
       "\n",
       "[32412 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# выводим выборочно две колонки\n",
    "\n",
    "display(df_test.loc[:, ['winter_night_cost', 'profit']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы вывели прибыльность каждого объекта в тестовом датафрейме без учёта депозита, чтобы получить общую прибыль: суммируем показания параметра `profit`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Суммарный профит забронированных номеров тестовой выборки: 58234560.0\n",
      "\n",
      "Суммарные потери отменённых забронированных номеров тестовой выборки: -17112820.0\n",
      "\n",
      "Общий суммарный профит тестовой выборки (за 8 мес): 41121740.0\n",
      "\n",
      "Общий суммарный профит тестовой выборки (за год): 61682610.0\n"
     ]
    }
   ],
   "source": [
    "# суммарный профит забронированных номеров тестовой выборки\n",
    "print('Суммарный профит забронированных номеров тестовой выборки:', df_test.loc[(df_test['is_canceled'] == 0)]['profit'].sum())\n",
    "print()\n",
    "\n",
    "# суммарный профит отменённых забронированных номеров тестовой выборки\n",
    "print('Суммарные потери отменённых забронированных номеров тестовой выборки:', df_test.loc[(df_test['is_canceled'] == 1)]['profit'].sum())\n",
    "print()\n",
    "\n",
    "# общий суммарный профит тестовой выборки за 8 мес (Январь-Август)\n",
    "print('Общий суммарный профит тестовой выборки (за 8 мес):', df_test['profit'].sum())\n",
    "print()\n",
    "\n",
    "# общий суммарный профит тестовой выборки в пересчёте на год\n",
    "print('Общий суммарный профит тестовой выборки (за год):', df_test['profit'].sum() * 12/8)\n",
    "\n",
    "# сохраняем в переменную\n",
    "profit_before_ml = df_test['profit'].sum() * 12/8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Вывод:` 29% от вырученых денег это потери связанные с издержками при отмене бронирования (уборка номера + стомость одной ночи)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.5. <a id='toc1_1_5_'></a>[Шаг 4. Разработка модели ML](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Делаем преобразование категориальных признаков в цисловые для обучения модели. Прямое кодирование."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>babies</th>\n",
       "      <th>meal</th>\n",
       "      <th>distribution_channel</th>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <th>previous_cancellations</th>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <th>reserved_room_type</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>total_nights</th>\n",
       "      <th>winter_night_cost</th>\n",
       "      <th>one_time_cleaning_winter</th>\n",
       "      <th>season_coeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>600</td>\n",
       "      <td>350</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>FB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>600</td>\n",
       "      <td>350</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0            0          7               2015               July   \n",
       "1            0         14               2015               July   \n",
       "2            0          0               2015               July   \n",
       "3            0          9               2015               July   \n",
       "4            1         85               2015               July   \n",
       "\n",
       "   arrival_date_day_of_month  stays_in_weekend_nights  stays_in_week_nights  \\\n",
       "0                          1                        0                     1   \n",
       "1                          1                        0                     2   \n",
       "2                          1                        0                     2   \n",
       "3                          1                        0                     2   \n",
       "4                          1                        0                     3   \n",
       "\n",
       "   adults  children  babies       meal distribution_channel  \\\n",
       "0       1         0       0  BB                      Direct   \n",
       "1       2         0       0  BB                       TA/TO   \n",
       "2       2         0       0  BB                      Direct   \n",
       "3       2         0       0  FB                      Direct   \n",
       "4       2         0       0  BB                       TA/TO   \n",
       "\n",
       "   is_repeated_guest  previous_cancellations  previous_bookings_not_canceled  \\\n",
       "0                  0                       0                               0   \n",
       "1                  0                       0                               0   \n",
       "2                  0                       0                               0   \n",
       "3                  0                       0                               0   \n",
       "4                  0                       0                               0   \n",
       "\n",
       "  reserved_room_type  booking_changes  days_in_waiting_list customer_type  \\\n",
       "0   A                               0                     0     Transient   \n",
       "1   A                               0                     0     Transient   \n",
       "2   C                               0                     0     Transient   \n",
       "3   C                               0                     0     Transient   \n",
       "4   A                               0                     0     Transient   \n",
       "\n",
       "   required_car_parking_spaces  total_of_special_requests  total_nights  \\\n",
       "0                            0                          0             1   \n",
       "1                            0                          1             2   \n",
       "2                            0                          0             2   \n",
       "3                            0                          1             2   \n",
       "4                            0                          1             3   \n",
       "\n",
       "   winter_night_cost  one_time_cleaning_winter  season_coeff  \n",
       "0               1000                       400           1.4  \n",
       "1               1000                       400           1.4  \n",
       "2                600                       350           1.4  \n",
       "3                600                       350           1.4  \n",
       "4               1000                       400           1.4  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(65229, 25)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(df_train.head())\n",
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# преобразуем df_train\n",
    "df_train_ohe = pd.get_dummies(df_train, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>total_nights</th>\n",
       "      <th>winter_night_cost</th>\n",
       "      <th>one_time_cleaning_winter</th>\n",
       "      <th>season_coeff</th>\n",
       "      <th>arrival_date_month_August</th>\n",
       "      <th>arrival_date_month_December</th>\n",
       "      <th>arrival_date_month_February</th>\n",
       "      <th>arrival_date_month_January</th>\n",
       "      <th>arrival_date_month_July</th>\n",
       "      <th>arrival_date_month_June</th>\n",
       "      <th>arrival_date_month_March</th>\n",
       "      <th>arrival_date_month_May</th>\n",
       "      <th>arrival_date_month_November</th>\n",
       "      <th>arrival_date_month_October</th>\n",
       "      <th>arrival_date_month_September</th>\n",
       "      <th>meal_FB</th>\n",
       "      <th>meal_HB</th>\n",
       "      <th>meal_SC</th>\n",
       "      <th>meal_SC</th>\n",
       "      <th>distribution_channel_Direct</th>\n",
       "      <th>distribution_channel_GDS</th>\n",
       "      <th>distribution_channel_TA/TO</th>\n",
       "      <th>reserved_room_type_B</th>\n",
       "      <th>reserved_room_type_C</th>\n",
       "      <th>reserved_room_type_D</th>\n",
       "      <th>reserved_room_type_E</th>\n",
       "      <th>reserved_room_type_F</th>\n",
       "      <th>reserved_room_type_G</th>\n",
       "      <th>customer_type_Group</th>\n",
       "      <th>customer_type_Transient</th>\n",
       "      <th>customer_type_Transient-Party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>600</td>\n",
       "      <td>350</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>600</td>\n",
       "      <td>350</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_canceled  lead_time  arrival_date_year  arrival_date_day_of_month  \\\n",
       "0            0          7               2015                          1   \n",
       "1            0         14               2015                          1   \n",
       "2            0          0               2015                          1   \n",
       "3            0          9               2015                          1   \n",
       "4            1         85               2015                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  children  babies  \\\n",
       "0                        0                     1       1         0       0   \n",
       "1                        0                     2       2         0       0   \n",
       "2                        0                     2       2         0       0   \n",
       "3                        0                     2       2         0       0   \n",
       "4                        0                     3       2         0       0   \n",
       "\n",
       "   is_repeated_guest  previous_cancellations  previous_bookings_not_canceled  \\\n",
       "0                  0                       0                               0   \n",
       "1                  0                       0                               0   \n",
       "2                  0                       0                               0   \n",
       "3                  0                       0                               0   \n",
       "4                  0                       0                               0   \n",
       "\n",
       "   booking_changes  days_in_waiting_list  required_car_parking_spaces  \\\n",
       "0                0                     0                            0   \n",
       "1                0                     0                            0   \n",
       "2                0                     0                            0   \n",
       "3                0                     0                            0   \n",
       "4                0                     0                            0   \n",
       "\n",
       "   total_of_special_requests  total_nights  winter_night_cost  \\\n",
       "0                          0             1               1000   \n",
       "1                          1             2               1000   \n",
       "2                          0             2                600   \n",
       "3                          1             2                600   \n",
       "4                          1             3               1000   \n",
       "\n",
       "   one_time_cleaning_winter  season_coeff  arrival_date_month_August  \\\n",
       "0                       400           1.4                          0   \n",
       "1                       400           1.4                          0   \n",
       "2                       350           1.4                          0   \n",
       "3                       350           1.4                          0   \n",
       "4                       400           1.4                          0   \n",
       "\n",
       "   arrival_date_month_December  arrival_date_month_February  \\\n",
       "0                            0                            0   \n",
       "1                            0                            0   \n",
       "2                            0                            0   \n",
       "3                            0                            0   \n",
       "4                            0                            0   \n",
       "\n",
       "   arrival_date_month_January  arrival_date_month_July  \\\n",
       "0                           0                        1   \n",
       "1                           0                        1   \n",
       "2                           0                        1   \n",
       "3                           0                        1   \n",
       "4                           0                        1   \n",
       "\n",
       "   arrival_date_month_June  arrival_date_month_March  arrival_date_month_May  \\\n",
       "0                        0                         0                       0   \n",
       "1                        0                         0                       0   \n",
       "2                        0                         0                       0   \n",
       "3                        0                         0                       0   \n",
       "4                        0                         0                       0   \n",
       "\n",
       "   arrival_date_month_November  arrival_date_month_October  \\\n",
       "0                            0                           0   \n",
       "1                            0                           0   \n",
       "2                            0                           0   \n",
       "3                            0                           0   \n",
       "4                            0                           0   \n",
       "\n",
       "   arrival_date_month_September  meal_FB         meal_HB         meal_SC  \\\n",
       "0                             0               0               0        0   \n",
       "1                             0               0               0        0   \n",
       "2                             0               0               0        0   \n",
       "3                             0               1               0        0   \n",
       "4                             0               0               0        0   \n",
       "\n",
       "   meal_SC         distribution_channel_Direct  distribution_channel_GDS  \\\n",
       "0               0                            1                         0   \n",
       "1               0                            0                         0   \n",
       "2               0                            1                         0   \n",
       "3               0                            1                         0   \n",
       "4               0                            0                         0   \n",
       "\n",
       "   distribution_channel_TA/TO  reserved_room_type_B                 \\\n",
       "0                           0                                    0   \n",
       "1                           1                                    0   \n",
       "2                           0                                    0   \n",
       "3                           0                                    0   \n",
       "4                           1                                    0   \n",
       "\n",
       "   reserved_room_type_C                 reserved_room_type_D                 \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    1                                    0   \n",
       "3                                    1                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   reserved_room_type_E                 reserved_room_type_F                 \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   reserved_room_type_G                 customer_type_Group  \\\n",
       "0                                    0                    0   \n",
       "1                                    0                    0   \n",
       "2                                    0                    0   \n",
       "3                                    0                    0   \n",
       "4                                    0                    0   \n",
       "\n",
       "   customer_type_Transient  customer_type_Transient-Party  \n",
       "0                        1                              0  \n",
       "1                        1                              0  \n",
       "2                        1                              0  \n",
       "3                        1                              0  \n",
       "4                        1                              0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(65229, 47)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(df_train_ohe.head())\n",
    "df_train_ohe.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>babies</th>\n",
       "      <th>meal</th>\n",
       "      <th>distribution_channel</th>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <th>previous_cancellations</th>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <th>reserved_room_type</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>total_nights</th>\n",
       "      <th>winter_night_cost</th>\n",
       "      <th>one_time_cleaning_winter</th>\n",
       "      <th>season_coeff</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>350</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>350</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0            1         74               2017            January   \n",
       "1            1         62               2017            January   \n",
       "2            1         62               2017            January   \n",
       "3            1         71               2017            January   \n",
       "4            1        172               2017            January   \n",
       "\n",
       "   arrival_date_day_of_month  stays_in_weekend_nights  stays_in_week_nights  \\\n",
       "0                          1                        1                     0   \n",
       "1                          1                        2                     2   \n",
       "2                          1                        2                     2   \n",
       "3                          1                        2                     2   \n",
       "4                          1                        2                     5   \n",
       "\n",
       "   adults  children  babies       meal distribution_channel  \\\n",
       "0       2         0       0  BB                       TA/TO   \n",
       "1       2         0       0  BB                       TA/TO   \n",
       "2       2         0       0  BB                       TA/TO   \n",
       "3       1         0       0  BB                       TA/TO   \n",
       "4       2         0       0  BB                       TA/TO   \n",
       "\n",
       "   is_repeated_guest  previous_cancellations  previous_bookings_not_canceled  \\\n",
       "0                  0                       0                               0   \n",
       "1                  0                       0                               0   \n",
       "2                  0                       0                               0   \n",
       "3                  0                       0                               0   \n",
       "4                  0                       0                               0   \n",
       "\n",
       "  reserved_room_type  booking_changes  days_in_waiting_list customer_type  \\\n",
       "0   A                               0                     0     Transient   \n",
       "1   A                               0                     0     Transient   \n",
       "2   A                               0                     0     Transient   \n",
       "3   A                               0                     0     Transient   \n",
       "4   A                               0                     0     Transient   \n",
       "\n",
       "   required_car_parking_spaces  total_of_special_requests  total_nights  \\\n",
       "0                            0                          0             1   \n",
       "1                            0                          1             4   \n",
       "2                            0                          1             4   \n",
       "3                            0                          1             4   \n",
       "4                            0                          0             7   \n",
       "\n",
       "   winter_night_cost  one_time_cleaning_winter  season_coeff  profit  \n",
       "0               1000                       400           1.0 -1400.0  \n",
       "1               1000                       400           1.0 -1400.0  \n",
       "2                600                       350           1.0  -950.0  \n",
       "3                600                       350           1.0  -950.0  \n",
       "4               1000                       400           1.0 -1400.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(32412, 26)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(df_test.head())\n",
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_ohe = pd.get_dummies(df_test, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>babies</th>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <th>previous_cancellations</th>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>total_nights</th>\n",
       "      <th>winter_night_cost</th>\n",
       "      <th>one_time_cleaning_winter</th>\n",
       "      <th>season_coeff</th>\n",
       "      <th>profit</th>\n",
       "      <th>arrival_date_month_August</th>\n",
       "      <th>arrival_date_month_February</th>\n",
       "      <th>arrival_date_month_January</th>\n",
       "      <th>arrival_date_month_July</th>\n",
       "      <th>arrival_date_month_June</th>\n",
       "      <th>arrival_date_month_March</th>\n",
       "      <th>arrival_date_month_May</th>\n",
       "      <th>meal_FB</th>\n",
       "      <th>meal_HB</th>\n",
       "      <th>meal_SC</th>\n",
       "      <th>meal_SC</th>\n",
       "      <th>distribution_channel_Direct</th>\n",
       "      <th>distribution_channel_GDS</th>\n",
       "      <th>distribution_channel_TA/TO</th>\n",
       "      <th>reserved_room_type_B</th>\n",
       "      <th>reserved_room_type_C</th>\n",
       "      <th>reserved_room_type_D</th>\n",
       "      <th>reserved_room_type_E</th>\n",
       "      <th>reserved_room_type_F</th>\n",
       "      <th>reserved_room_type_G</th>\n",
       "      <th>customer_type_Group</th>\n",
       "      <th>customer_type_Transient</th>\n",
       "      <th>customer_type_Transient-Party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>350</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-950.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>350</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-950.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_canceled  lead_time  arrival_date_year  arrival_date_day_of_month  \\\n",
       "0            1         74               2017                          1   \n",
       "1            1         62               2017                          1   \n",
       "2            1         62               2017                          1   \n",
       "3            1         71               2017                          1   \n",
       "4            1        172               2017                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  children  babies  \\\n",
       "0                        1                     0       2         0       0   \n",
       "1                        2                     2       2         0       0   \n",
       "2                        2                     2       2         0       0   \n",
       "3                        2                     2       1         0       0   \n",
       "4                        2                     5       2         0       0   \n",
       "\n",
       "   is_repeated_guest  previous_cancellations  previous_bookings_not_canceled  \\\n",
       "0                  0                       0                               0   \n",
       "1                  0                       0                               0   \n",
       "2                  0                       0                               0   \n",
       "3                  0                       0                               0   \n",
       "4                  0                       0                               0   \n",
       "\n",
       "   booking_changes  days_in_waiting_list  required_car_parking_spaces  \\\n",
       "0                0                     0                            0   \n",
       "1                0                     0                            0   \n",
       "2                0                     0                            0   \n",
       "3                0                     0                            0   \n",
       "4                0                     0                            0   \n",
       "\n",
       "   total_of_special_requests  total_nights  winter_night_cost  \\\n",
       "0                          0             1               1000   \n",
       "1                          1             4               1000   \n",
       "2                          1             4                600   \n",
       "3                          1             4                600   \n",
       "4                          0             7               1000   \n",
       "\n",
       "   one_time_cleaning_winter  season_coeff  profit  arrival_date_month_August  \\\n",
       "0                       400           1.0 -1400.0                          0   \n",
       "1                       400           1.0 -1400.0                          0   \n",
       "2                       350           1.0  -950.0                          0   \n",
       "3                       350           1.0  -950.0                          0   \n",
       "4                       400           1.0 -1400.0                          0   \n",
       "\n",
       "   arrival_date_month_February  arrival_date_month_January  \\\n",
       "0                            0                           1   \n",
       "1                            0                           1   \n",
       "2                            0                           1   \n",
       "3                            0                           1   \n",
       "4                            0                           1   \n",
       "\n",
       "   arrival_date_month_July  arrival_date_month_June  arrival_date_month_March  \\\n",
       "0                        0                        0                         0   \n",
       "1                        0                        0                         0   \n",
       "2                        0                        0                         0   \n",
       "3                        0                        0                         0   \n",
       "4                        0                        0                         0   \n",
       "\n",
       "   arrival_date_month_May  meal_FB         meal_HB         meal_SC  \\\n",
       "0                       0               0               0        0   \n",
       "1                       0               0               0        0   \n",
       "2                       0               0               0        0   \n",
       "3                       0               0               0        0   \n",
       "4                       0               0               0        0   \n",
       "\n",
       "   meal_SC         distribution_channel_Direct  distribution_channel_GDS  \\\n",
       "0               0                            0                         0   \n",
       "1               0                            0                         0   \n",
       "2               0                            0                         0   \n",
       "3               0                            0                         0   \n",
       "4               0                            0                         0   \n",
       "\n",
       "   distribution_channel_TA/TO  reserved_room_type_B                 \\\n",
       "0                           1                                    0   \n",
       "1                           1                                    0   \n",
       "2                           1                                    0   \n",
       "3                           1                                    0   \n",
       "4                           1                                    0   \n",
       "\n",
       "   reserved_room_type_C                 reserved_room_type_D                 \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   reserved_room_type_E                 reserved_room_type_F                 \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   reserved_room_type_G                 customer_type_Group  \\\n",
       "0                                    0                    0   \n",
       "1                                    0                    0   \n",
       "2                                    0                    0   \n",
       "3                                    0                    0   \n",
       "4                                    0                    0   \n",
       "\n",
       "   customer_type_Transient  customer_type_Transient-Party  \n",
       "0                        1                              0  \n",
       "1                        1                              0  \n",
       "2                        1                              0  \n",
       "3                        1                              0  \n",
       "4                        1                              0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(32412, 44)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(df_test_ohe.head())\n",
    "df_test_ohe.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверяем разницу в параметрах между датасетами после кодировки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_571/860116454.py:3: FutureWarning: Index.__xor__ operating as a set operation is deprecated, in the future this will be a logical operation matching Series.__xor__.  Use index.symmetric_difference(other) instead\n",
      "  df_test_ohe.columns ^ df_train_ohe.columns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['arrival_date_month_December', 'arrival_date_month_November',\n",
       "       'arrival_date_month_October', 'arrival_date_month_September', 'profit'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# мы видим что пять параметров отсутствуют в обеих датасетах одновременно\n",
    "\n",
    "df_test_ohe.columns ^ df_train_ohe.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Добавляем недостающие параметры в кодированные датасеты:\n",
    "\n",
    "четыре параметра добавляем в `df_test_ohe`\n",
    "один параметр в `df_train_ohe`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_test_ohe\n",
    "df_test_ohe.insert(22, 'arrival_date_month_December', 0)\n",
    "df_test_ohe.insert(29, 'arrival_date_month_November', 0)\n",
    "df_test_ohe.insert(30, 'arrival_date_month_October', 0)\n",
    "df_test_ohe.insert(31, 'arrival_date_month_September', 0)\n",
    "\n",
    "# df_train_ohe\n",
    "df_train_ohe.insert(20, 'profit', 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>babies</th>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <th>previous_cancellations</th>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>total_nights</th>\n",
       "      <th>winter_night_cost</th>\n",
       "      <th>one_time_cleaning_winter</th>\n",
       "      <th>season_coeff</th>\n",
       "      <th>profit</th>\n",
       "      <th>arrival_date_month_August</th>\n",
       "      <th>arrival_date_month_December</th>\n",
       "      <th>arrival_date_month_February</th>\n",
       "      <th>arrival_date_month_January</th>\n",
       "      <th>arrival_date_month_July</th>\n",
       "      <th>arrival_date_month_June</th>\n",
       "      <th>arrival_date_month_March</th>\n",
       "      <th>arrival_date_month_May</th>\n",
       "      <th>arrival_date_month_November</th>\n",
       "      <th>arrival_date_month_October</th>\n",
       "      <th>arrival_date_month_September</th>\n",
       "      <th>meal_FB</th>\n",
       "      <th>meal_HB</th>\n",
       "      <th>meal_SC</th>\n",
       "      <th>meal_SC</th>\n",
       "      <th>distribution_channel_Direct</th>\n",
       "      <th>distribution_channel_GDS</th>\n",
       "      <th>distribution_channel_TA/TO</th>\n",
       "      <th>reserved_room_type_B</th>\n",
       "      <th>reserved_room_type_C</th>\n",
       "      <th>reserved_room_type_D</th>\n",
       "      <th>reserved_room_type_E</th>\n",
       "      <th>reserved_room_type_F</th>\n",
       "      <th>reserved_room_type_G</th>\n",
       "      <th>customer_type_Group</th>\n",
       "      <th>customer_type_Transient</th>\n",
       "      <th>customer_type_Transient-Party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>-1400.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_canceled  lead_time  arrival_date_year  arrival_date_day_of_month  \\\n",
       "0            1         74               2017                          1   \n",
       "1            1         62               2017                          1   \n",
       "2            1         62               2017                          1   \n",
       "3            1         71               2017                          1   \n",
       "4            1        172               2017                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  children  babies  \\\n",
       "0                        1                     0       2         0       0   \n",
       "1                        2                     2       2         0       0   \n",
       "2                        2                     2       2         0       0   \n",
       "3                        2                     2       1         0       0   \n",
       "4                        2                     5       2         0       0   \n",
       "\n",
       "   is_repeated_guest  previous_cancellations  previous_bookings_not_canceled  \\\n",
       "0                  0                       0                               0   \n",
       "1                  0                       0                               0   \n",
       "2                  0                       0                               0   \n",
       "3                  0                       0                               0   \n",
       "4                  0                       0                               0   \n",
       "\n",
       "   booking_changes  days_in_waiting_list  required_car_parking_spaces  \\\n",
       "0                0                     0                            0   \n",
       "1                0                     0                            0   \n",
       "2                0                     0                            0   \n",
       "3                0                     0                            0   \n",
       "4                0                     0                            0   \n",
       "\n",
       "   total_of_special_requests  total_nights  winter_night_cost  \\\n",
       "0                          0             1               1000   \n",
       "1                          1             4               1000   \n",
       "2                          1             4                600   \n",
       "3                          1             4                600   \n",
       "4                          0             7               1000   \n",
       "\n",
       "   one_time_cleaning_winter  season_coeff  profit  arrival_date_month_August  \\\n",
       "0                       400           1.0 -1400.0                          0   \n",
       "1                       400           1.0 -1400.0                          0   \n",
       "2                       350           1.0  -950.0                          0   \n",
       "3                       350           1.0  -950.0                          0   \n",
       "4                       400           1.0 -1400.0                          0   \n",
       "\n",
       "   arrival_date_month_December  arrival_date_month_February  \\\n",
       "0                            0                            0   \n",
       "1                            0                            0   \n",
       "2                            0                            0   \n",
       "3                            0                            0   \n",
       "4                            0                            0   \n",
       "\n",
       "   arrival_date_month_January  arrival_date_month_July  \\\n",
       "0                           1                        0   \n",
       "1                           1                        0   \n",
       "2                           1                        0   \n",
       "3                           1                        0   \n",
       "4                           1                        0   \n",
       "\n",
       "   arrival_date_month_June  arrival_date_month_March  arrival_date_month_May  \\\n",
       "0                        0                         0                       0   \n",
       "1                        0                         0                       0   \n",
       "2                        0                         0                       0   \n",
       "3                        0                         0                       0   \n",
       "4                        0                         0                       0   \n",
       "\n",
       "   arrival_date_month_November  arrival_date_month_October  \\\n",
       "0                            0                           0   \n",
       "1                            0                           0   \n",
       "2                            0                           0   \n",
       "3                            0                           0   \n",
       "4                            0                           0   \n",
       "\n",
       "   arrival_date_month_September  meal_FB         meal_HB         meal_SC  \\\n",
       "0                             0               0               0        0   \n",
       "1                             0               0               0        0   \n",
       "2                             0               0               0        0   \n",
       "3                             0               0               0        0   \n",
       "4                             0               0               0        0   \n",
       "\n",
       "   meal_SC         distribution_channel_Direct  distribution_channel_GDS  \\\n",
       "0               0                            0                         0   \n",
       "1               0                            0                         0   \n",
       "2               0                            0                         0   \n",
       "3               0                            0                         0   \n",
       "4               0                            0                         0   \n",
       "\n",
       "   distribution_channel_TA/TO  reserved_room_type_B                 \\\n",
       "0                           1                                    0   \n",
       "1                           1                                    0   \n",
       "2                           1                                    0   \n",
       "3                           1                                    0   \n",
       "4                           1                                    0   \n",
       "\n",
       "   reserved_room_type_C                 reserved_room_type_D                 \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   reserved_room_type_E                 reserved_room_type_F                 \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   reserved_room_type_G                 customer_type_Group  \\\n",
       "0                                    0                    0   \n",
       "1                                    0                    0   \n",
       "2                                    0                    0   \n",
       "3                                    0                    0   \n",
       "4                                    0                    0   \n",
       "\n",
       "   customer_type_Transient  customer_type_Transient-Party  \n",
       "0                        1                              0  \n",
       "1                        1                              0  \n",
       "2                        1                              0  \n",
       "3                        1                              0  \n",
       "4                        1                              0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(32412, 48)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(df_test_ohe.head())\n",
    "df_test_ohe.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>babies</th>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <th>previous_cancellations</th>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>total_nights</th>\n",
       "      <th>winter_night_cost</th>\n",
       "      <th>one_time_cleaning_winter</th>\n",
       "      <th>season_coeff</th>\n",
       "      <th>profit</th>\n",
       "      <th>arrival_date_month_August</th>\n",
       "      <th>arrival_date_month_December</th>\n",
       "      <th>arrival_date_month_February</th>\n",
       "      <th>arrival_date_month_January</th>\n",
       "      <th>arrival_date_month_July</th>\n",
       "      <th>arrival_date_month_June</th>\n",
       "      <th>arrival_date_month_March</th>\n",
       "      <th>arrival_date_month_May</th>\n",
       "      <th>arrival_date_month_November</th>\n",
       "      <th>arrival_date_month_October</th>\n",
       "      <th>arrival_date_month_September</th>\n",
       "      <th>meal_FB</th>\n",
       "      <th>meal_HB</th>\n",
       "      <th>meal_SC</th>\n",
       "      <th>meal_SC</th>\n",
       "      <th>distribution_channel_Direct</th>\n",
       "      <th>distribution_channel_GDS</th>\n",
       "      <th>distribution_channel_TA/TO</th>\n",
       "      <th>reserved_room_type_B</th>\n",
       "      <th>reserved_room_type_C</th>\n",
       "      <th>reserved_room_type_D</th>\n",
       "      <th>reserved_room_type_E</th>\n",
       "      <th>reserved_room_type_F</th>\n",
       "      <th>reserved_room_type_G</th>\n",
       "      <th>customer_type_Group</th>\n",
       "      <th>customer_type_Transient</th>\n",
       "      <th>customer_type_Transient-Party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>600</td>\n",
       "      <td>350</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>600</td>\n",
       "      <td>350</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_canceled  lead_time  arrival_date_year  arrival_date_day_of_month  \\\n",
       "0            0          7               2015                          1   \n",
       "1            0         14               2015                          1   \n",
       "2            0          0               2015                          1   \n",
       "3            0          9               2015                          1   \n",
       "4            1         85               2015                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  children  babies  \\\n",
       "0                        0                     1       1         0       0   \n",
       "1                        0                     2       2         0       0   \n",
       "2                        0                     2       2         0       0   \n",
       "3                        0                     2       2         0       0   \n",
       "4                        0                     3       2         0       0   \n",
       "\n",
       "   is_repeated_guest  previous_cancellations  previous_bookings_not_canceled  \\\n",
       "0                  0                       0                               0   \n",
       "1                  0                       0                               0   \n",
       "2                  0                       0                               0   \n",
       "3                  0                       0                               0   \n",
       "4                  0                       0                               0   \n",
       "\n",
       "   booking_changes  days_in_waiting_list  required_car_parking_spaces  \\\n",
       "0                0                     0                            0   \n",
       "1                0                     0                            0   \n",
       "2                0                     0                            0   \n",
       "3                0                     0                            0   \n",
       "4                0                     0                            0   \n",
       "\n",
       "   total_of_special_requests  total_nights  winter_night_cost  \\\n",
       "0                          0             1               1000   \n",
       "1                          1             2               1000   \n",
       "2                          0             2                600   \n",
       "3                          1             2                600   \n",
       "4                          1             3               1000   \n",
       "\n",
       "   one_time_cleaning_winter  season_coeff  profit  arrival_date_month_August  \\\n",
       "0                       400           1.4       0                          0   \n",
       "1                       400           1.4       0                          0   \n",
       "2                       350           1.4       0                          0   \n",
       "3                       350           1.4       0                          0   \n",
       "4                       400           1.4       0                          0   \n",
       "\n",
       "   arrival_date_month_December  arrival_date_month_February  \\\n",
       "0                            0                            0   \n",
       "1                            0                            0   \n",
       "2                            0                            0   \n",
       "3                            0                            0   \n",
       "4                            0                            0   \n",
       "\n",
       "   arrival_date_month_January  arrival_date_month_July  \\\n",
       "0                           0                        1   \n",
       "1                           0                        1   \n",
       "2                           0                        1   \n",
       "3                           0                        1   \n",
       "4                           0                        1   \n",
       "\n",
       "   arrival_date_month_June  arrival_date_month_March  arrival_date_month_May  \\\n",
       "0                        0                         0                       0   \n",
       "1                        0                         0                       0   \n",
       "2                        0                         0                       0   \n",
       "3                        0                         0                       0   \n",
       "4                        0                         0                       0   \n",
       "\n",
       "   arrival_date_month_November  arrival_date_month_October  \\\n",
       "0                            0                           0   \n",
       "1                            0                           0   \n",
       "2                            0                           0   \n",
       "3                            0                           0   \n",
       "4                            0                           0   \n",
       "\n",
       "   arrival_date_month_September  meal_FB         meal_HB         meal_SC  \\\n",
       "0                             0               0               0        0   \n",
       "1                             0               0               0        0   \n",
       "2                             0               0               0        0   \n",
       "3                             0               1               0        0   \n",
       "4                             0               0               0        0   \n",
       "\n",
       "   meal_SC         distribution_channel_Direct  distribution_channel_GDS  \\\n",
       "0               0                            1                         0   \n",
       "1               0                            0                         0   \n",
       "2               0                            1                         0   \n",
       "3               0                            1                         0   \n",
       "4               0                            0                         0   \n",
       "\n",
       "   distribution_channel_TA/TO  reserved_room_type_B                 \\\n",
       "0                           0                                    0   \n",
       "1                           1                                    0   \n",
       "2                           0                                    0   \n",
       "3                           0                                    0   \n",
       "4                           1                                    0   \n",
       "\n",
       "   reserved_room_type_C                 reserved_room_type_D                 \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    1                                    0   \n",
       "3                                    1                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   reserved_room_type_E                 reserved_room_type_F                 \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   reserved_room_type_G                 customer_type_Group  \\\n",
       "0                                    0                    0   \n",
       "1                                    0                    0   \n",
       "2                                    0                    0   \n",
       "3                                    0                    0   \n",
       "4                                    0                    0   \n",
       "\n",
       "   customer_type_Transient  customer_type_Transient-Party  \n",
       "0                        1                              0  \n",
       "1                        1                              0  \n",
       "2                        1                              0  \n",
       "3                        1                              0  \n",
       "4                        1                              0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(65229, 48)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(df_train_ohe.head())\n",
    "df_train_ohe.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проеверяем что нет колонок присутствующих только в одном датафрейме"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_571/652679777.py:1: FutureWarning: Index.__xor__ operating as a set operation is deprecated, in the future this will be a logical operation matching Series.__xor__.  Use index.symmetric_difference(other) instead\n",
      "  df_test_ohe.columns ^ df_train_ohe.columns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_ohe.columns ^ df_train_ohe.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Параметры в обоих датафреймах совпадают. Приступаем к разделению на целевую и нецелевые выбрки для тренировочного `df_train` и тестового датафрейма `df_test`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Делим тренировочную и валидационную выборки на целевые и нецелевые выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# извлекаем признаки, исключаем целевой признак и признаки которые не имеет предсказательной силы \n",
    "features_train = df_train_ohe.drop(['is_canceled'], axis=1) \n",
    "    \n",
    "# извлекаем целевой признак\n",
    "target_train = df_train_ohe['is_canceled'] \n",
    "\n",
    "# извлекаем признаки, исключаем целевой признак и признаки которые не имеет предсказательной силы \n",
    "features_test = df_test_ohe.drop(['is_canceled'], axis=1)\n",
    "    \n",
    "# извлекаем целевой признак\n",
    "target_test = df_test_ohe['is_canceled'] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выводим размеры полученых выборок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(65229, 47)\n",
      "(65229,)\n",
      "(32412, 47)\n",
      "(32412,)\n"
     ]
    }
   ],
   "source": [
    "print(features_train.shape)\n",
    "print(target_train.shape)\n",
    "print(features_test.shape)\n",
    "print(target_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проводим масштабирование "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(features_train)\n",
    "\n",
    "features_train = scaler.transform(features_train)\n",
    "features_test = scaler.transform(features_test)\n",
    "\n",
    "pd.options.mode.chained_assignment = None\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Выбор метрики для моделей:` самой важной метрикой для задачи является `полнота (Recall)` тк эта метрика определяет количество истинно положительных среди всех меток класса, которые были определены как «положительный». Эта оценочная метрика особенно важна когда в поставленной задаче ошибка нераспознания положительного класса высока. В нашем случае, нераспознание положительного класса приведёт неверной к оценке отказавшихся от бронирования постояльцев и как результат неверный прогноз отказников. Это приведёт к непредвиденным убыткам."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшая полнота               0.30983093264189543\n"
     ]
    }
   ],
   "source": [
    "# инициализация модели\n",
    "model = LogisticRegression(random_state=12345, solver='lbfgs', max_iter=2000)\n",
    "# обучение модели\n",
    "model.fit(features_train, target_train)\n",
    "\n",
    "# предсказания модели\n",
    "predictions_logistic = model.predict(features_train)\n",
    "\n",
    "probabilities_valid = model.predict_proba(features_train)\n",
    "probabilities_one_valid = probabilities_valid[:, 1]\n",
    "\n",
    "# кроссвалидация с пятью блоками\n",
    "mean_score = cross_val_score(model, features_train, target_train, cv=5, scoring='recall').mean()\n",
    "\n",
    "# Вывод метрики Полноты\n",
    "print('Лучшая полнота              ', mean_score)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшая модель:                            DecisionTreeClassifier(max_depth=4, random_state=12345)\n",
      "Лучший критерий:                          gini\n",
      "Лучшая полнота:                           0.636303974902853\n"
     ]
    }
   ],
   "source": [
    "# создаём переменные для выявления лучшей модели и лучшего результата\n",
    "best_model = None\n",
    "best_mean_score = 0\n",
    "best_criterion = 0\n",
    "\n",
    "\n",
    "creteria_list = ['gini', 'entropy']\n",
    "# создаём цикл для перебора гиперпараметра criterion\n",
    "for crit in creteria_list:\n",
    "\t# создаём цикл для перебора глубины дерева\n",
    "\tfor depth in range(1, 20):\n",
    "\t\t# создам переменную и записываем в неё модель с перебором глубины деревьев и добавлением случайности для улучшения результата\n",
    "\t\tmodel = DecisionTreeClassifier(criterion=crit, random_state=12345, max_depth= depth)\n",
    "\n",
    "\t\t# обучаем модель с заданной глубиной дерева\n",
    "\t\tmodel.fit(features_train, target_train)\n",
    "\n",
    "\t\t# высчитываем предсказания модели на основе валидационной выборки\n",
    "\t\tpredictions_dt = model.predict(features_train)\n",
    "\t\tprobabilities_valid = model.predict_proba(features_train)\n",
    "\t\tprobabilities_one_valid = probabilities_valid[:, 1]\n",
    "\t\t\n",
    "\t\n",
    "\t\tmean_score = cross_val_score(model, features_train, target_train, cv=5, scoring='recall').mean()\n",
    "\t\t\n",
    "\t\t# находим лучшую модель\n",
    "\t\tif mean_score > best_mean_score:\n",
    "\t\t\tbest_criterion = crit\n",
    "\t\t\tbest_mean_score = mean_score\n",
    "\t\t\tbest_model = model\n",
    "\t\t\t\n",
    "\t\t\n",
    "\t\t\n",
    "# выдод результатов классификации    \n",
    "print('Лучшая модель:                           ', best_model)\n",
    "print('Лучший критерий:                         ', best_criterion)\n",
    "print('Лучшая полнота:                          ', best_mean_score)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество деревьев (est):                        47\n",
      "Глубина дерева (depth):                           9\n",
      "Лучшая полнота:                                   0.3952128375491184\n"
     ]
    }
   ],
   "source": [
    "# создаём переменные для выявления лучшей модели, лучшего результата\n",
    "# создаём счётчики кол-ва деревьев и глубины деревьев чтобы выявить сочетание с лучшим показателем accuracy\n",
    "best_model = None\n",
    "n_est = 0\n",
    "max_depth = 0\n",
    "best_recall = 0\n",
    "best_final_score = 0\n",
    "\n",
    "\n",
    "# создаём цикл с перебором по кол-ву деревьев\n",
    "for est in range(40, 50):\n",
    "    # создаём вложенный цикл с перебором глубины каждого дерева\n",
    "    for depth in range(1, 10):\n",
    "\n",
    "        # создам переменную и записываем в неё модель с перебором кол-ва деревьев, \n",
    "        # глубины деревьев и добавлением случайности для улучшения результата\n",
    "        model = RandomForestClassifier(random_state=12345, n_estimators=est, max_depth=depth)\n",
    "\n",
    "        # обучаем модель на тренировочной выборке\n",
    "        model.fit(features_train, target_train)\n",
    "\n",
    "        # высчитываем предсказания модели на основе тренировочной выборки\n",
    "        predictions = model.predict(features_train)\n",
    "\n",
    "        \n",
    "        # проводим кросс-валидацию\n",
    "        scores = cross_val_score(model, features_train, target_train, cv=5, scoring='recall')\n",
    "        final_score = scores.mean()\n",
    "\n",
    "        probabilities_valid = model.predict_proba(features_train)\n",
    "        probabilities_one_valid = probabilities_valid[:, 1]\n",
    "\n",
    "\n",
    "        # находим лучшую модель, находим оптимальное кол-во деревьев и их глубину\n",
    "        if final_score > best_final_score:\n",
    "            best_final_score = final_score\n",
    "            n_est = est\n",
    "            max_depth = depth\n",
    "            best_model = model\n",
    "        \n",
    "\n",
    "# выдод результатов классификации случайного леса\n",
    "\n",
    "print('Количество деревьев (est):                       ', n_est) \n",
    "print('Глубина дерева (depth):                          ', max_depth)\n",
    "print('Лучшая полнота:                                  ', best_final_score)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Вывод:` \n",
    "\n",
    "По результатам обучения моделей, лучшей моделью с самыми высокими метриками полноты оказалось `Дерево решений`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1.5.1. <a id='toc1_1_5_1_'></a>[Применение лучшей модели на тестовой выборке](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Применяем дерево решений для `df_test` выборки с наилучшими параметрами: `DecisionTreeClassifier(max_depth=19, random_state=12345)` criterion='gini'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшая (Accuracy):                          0.7234357645316549\n",
      "Лучшая F1-мера:                             0.6163984936665525\n",
      "Лучшая полнота:                             0.5719958700659201\n",
      "Лучшая ROC_AUC:                             0.7378422634304449\n"
     ]
    }
   ],
   "source": [
    "# создам переменную и записываем в неё модель с лучшими показателями пртестированными на тренировочной выборке.\n",
    "model = DecisionTreeClassifier(criterion='gini', random_state=12345, max_depth= 4) \n",
    "\n",
    "# обучаем модель с заданной глубиной дерева\n",
    "model.fit(features_train, target_train)\n",
    "\n",
    "# высчитываем предсказания модели на основе валидационной выборки\n",
    "predictions_dt_test = model.predict(features_test)\n",
    "\n",
    "probabilities_test = model.predict_proba(features_test)\n",
    "probabilities_one_test = probabilities_test[:, 1]\n",
    "\n",
    "# находим качество модели сравнивая результаты валидационой выборки и результаты предсказательной модели\n",
    "result = accuracy_score(target_test, predictions_dt_test)\n",
    "\n",
    "# сохраняем значения метрик в переменные\n",
    "f1 = f1_score(target_test, predictions_dt_test)\n",
    "recall = recall_score(target_test, predictions_dt_test)\n",
    "roc = roc_auc_score(target_test, probabilities_one_test)\n",
    "\n",
    "\t\t\n",
    "# выдод результатов классификации    \n",
    "   \n",
    "print('Лучшая (Accuracy):                         ', result)\n",
    "print('Лучшая F1-мера:                            ', f1)\n",
    "print('Лучшая полнота:                            ', recall)\n",
    "print('Лучшая ROC_AUC:                            ', roc)\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверяем тип предсказаний и меняем на Series для того чтобы добавить предсказания в таблицу `df_test`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# проверяем тип данных\n",
    "print(type(predictions_dt_test))\n",
    "\n",
    "# меняем тип данных\n",
    "predictions_dt_test_series = pd.Series(predictions_dt_test)\n",
    "\n",
    "# проверяем смену типа на Series\n",
    "type(predictions_dt_test_series)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Добавляем в `df_test` новый параметр `is_canceled_predicted` с предсказаниями модели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test['is_canceled_predicted'] = predictions_dt_test_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выводим таблицу для просмотра"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>babies</th>\n",
       "      <th>meal</th>\n",
       "      <th>distribution_channel</th>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <th>previous_cancellations</th>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <th>reserved_room_type</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>total_nights</th>\n",
       "      <th>winter_night_cost</th>\n",
       "      <th>one_time_cleaning_winter</th>\n",
       "      <th>season_coeff</th>\n",
       "      <th>profit</th>\n",
       "      <th>is_canceled_predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1400.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>BB</td>\n",
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       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1400.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>350</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-950.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>350</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-950.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>2017</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
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       "      <td>TA/TO</td>\n",
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       "      <td>Transient</td>\n",
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       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1000</td>\n",
       "      <td>400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1400.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0            1         74               2017            January   \n",
       "1            1         62               2017            January   \n",
       "2            1         62               2017            January   \n",
       "3            1         71               2017            January   \n",
       "4            1        172               2017            January   \n",
       "\n",
       "   arrival_date_day_of_month  stays_in_weekend_nights  stays_in_week_nights  \\\n",
       "0                          1                        1                     0   \n",
       "1                          1                        2                     2   \n",
       "2                          1                        2                     2   \n",
       "3                          1                        2                     2   \n",
       "4                          1                        2                     5   \n",
       "\n",
       "   adults  children  babies       meal distribution_channel  \\\n",
       "0       2         0       0  BB                       TA/TO   \n",
       "1       2         0       0  BB                       TA/TO   \n",
       "2       2         0       0  BB                       TA/TO   \n",
       "3       1         0       0  BB                       TA/TO   \n",
       "4       2         0       0  BB                       TA/TO   \n",
       "\n",
       "   is_repeated_guest  previous_cancellations  previous_bookings_not_canceled  \\\n",
       "0                  0                       0                               0   \n",
       "1                  0                       0                               0   \n",
       "2                  0                       0                               0   \n",
       "3                  0                       0                               0   \n",
       "4                  0                       0                               0   \n",
       "\n",
       "  reserved_room_type  booking_changes  days_in_waiting_list customer_type  \\\n",
       "0   A                               0                     0     Transient   \n",
       "1   A                               0                     0     Transient   \n",
       "2   A                               0                     0     Transient   \n",
       "3   A                               0                     0     Transient   \n",
       "4   A                               0                     0     Transient   \n",
       "\n",
       "   required_car_parking_spaces  total_of_special_requests  total_nights  \\\n",
       "0                            0                          0             1   \n",
       "1                            0                          1             4   \n",
       "2                            0                          1             4   \n",
       "3                            0                          1             4   \n",
       "4                            0                          0             7   \n",
       "\n",
       "   winter_night_cost  one_time_cleaning_winter  season_coeff  profit  \\\n",
       "0               1000                       400           1.0 -1400.0   \n",
       "1               1000                       400           1.0 -1400.0   \n",
       "2                600                       350           1.0  -950.0   \n",
       "3                600                       350           1.0  -950.0   \n",
       "4               1000                       400           1.0 -1400.0   \n",
       "\n",
       "   is_canceled_predicted  \n",
       "0                      1  \n",
       "1                      0  \n",
       "2                      0  \n",
       "3                      0  \n",
       "4                      1  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Подсчёт прибыли"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "def profit_predicted(row, col):\n",
    "    if row['is_canceled'] == 0:\n",
    "        income = row['winter_night_cost'] * row['season_coeff'] * row['total_nights']\n",
    "        expences = row['one_time_cleaning_winter'] * round(1 + row['total_nights'] // 2)\n",
    "    elif(row[col] == 0) and (row['is_canceled'] ==1 ):\n",
    "        income = 0 #бронь отменена, прибыль отсутствует\n",
    "        expences = row['one_time_cleaning_winter'] + row['winter_night_cost'] * row['season_coeff']\n",
    "        \n",
    "    elif (row[col] == 1) and (row['is_canceled']==1):\n",
    "        # вариант 3 если система верно предсказала отмену брони, то клиенту предлагается депозит,\n",
    "        income= row['winter_night_cost'] * 0.8 * row['season_coeff'] + row['one_time_cleaning_winter'] * 0.8\n",
    "        # а в расход идет только разовое обслуживание\n",
    "        expences = row['one_time_cleaning_winter'] + row['winter_night_cost'] * row['season_coeff']\n",
    "    profit_total = income - expences\n",
    "    return profit_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Суммарный профит тестовой выборки (за год) до прогнозирования:                 61682610.0\n",
      "Суммарный профит тестовой выборки после ML (за 8 мес):                         44049248.0\n",
      "Суммарный профит тестовой выборки после ML (за год):                           66073872.0\n",
      "Суммарный профит тестовой выборки (за год) после ML с учётом бюджета 400к:     65673872.0\n",
      "Выигрыш от внедрения модели (за год) с учётом  бюджета 400к:                   3991262.0\n"
     ]
    }
   ],
   "source": [
    "df_test['profit_predicted'] = df_test.apply(profit_predicted, col='is_canceled_predicted', axis = 1)\n",
    "profit_predicted2 = df_test['profit_predicted'].sum()\n",
    "\n",
    "print('Суммарный профит тестовой выборки (за год) до прогнозирования:                ',  profit_before_ml)\n",
    "#print()\n",
    "print('Суммарный профит тестовой выборки после ML (за 8 мес):                        ',  profit_predicted2)\n",
    "#print()\n",
    "print('Суммарный профит тестовой выборки после ML (за год):                          ',  profit_predicted2 * 12/8)\n",
    "#print()\n",
    "print('Суммарный профит тестовой выборки (за год) после ML с учётом бюджета 400к:    ',  profit_predicted2 * 12/8 - BUDGET)\n",
    "#print()\n",
    "print('Выигрыш от внедрения модели (за год) с учётом  бюджета 400к:                  ',  (profit_predicted2 * 12/8 - profit_before_ml) - BUDGET)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Вывод:` В результате применения модели `дерево решений` нам удалось получить предсказания отказа от бронирования клиентов, что повысило прогностическую способность и привело к повышению прибыли отеля с 61682610.0 руб. до 65673872.0 руб. с учётом затраченного бюджета 400 000 на создание модели.\n",
    "\n",
    "В итоге, с учётом затрат на создание модели, прибыль отеля выросла после применения модели выросла на 3991262.0 руб."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.6. <a id='toc1_1_6_'></a>[Шаг 5. Опишите портрет «ненадёжного» клиента](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создаём список признаков для определения их влияния на целевой признак"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['is_canceled', 'lead_time', 'arrival_date_year',\n",
       "       'arrival_date_day_of_month', 'stays_in_weekend_nights',\n",
       "       'stays_in_week_nights', 'adults', 'children', 'babies',\n",
       "       'is_repeated_guest', 'previous_cancellations',\n",
       "       'previous_bookings_not_canceled', 'booking_changes',\n",
       "       'days_in_waiting_list', 'required_car_parking_spaces',\n",
       "       'total_of_special_requests', 'total_nights', 'winter_night_cost',\n",
       "       'one_time_cleaning_winter', 'season_coeff', 'arrival_date_month_August',\n",
       "       'arrival_date_month_December', 'arrival_date_month_February',\n",
       "       'arrival_date_month_January', 'arrival_date_month_July',\n",
       "       'arrival_date_month_June', 'arrival_date_month_March',\n",
       "       'arrival_date_month_May', 'arrival_date_month_November',\n",
       "       'arrival_date_month_October', 'arrival_date_month_September',\n",
       "       'meal_FB       ', 'meal_HB       ', 'meal_SC', 'meal_SC       ',\n",
       "       'distribution_channel_Direct', 'distribution_channel_GDS',\n",
       "       'distribution_channel_TA/TO', 'reserved_room_type_B               ',\n",
       "       'reserved_room_type_C               ',\n",
       "       'reserved_room_type_D               ',\n",
       "       'reserved_room_type_E               ',\n",
       "       'reserved_room_type_F               ',\n",
       "       'reserved_room_type_G               ', 'customer_type_Group',\n",
       "       'customer_type_Transient', 'customer_type_Transient-Party'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_ohe_col = df_test_ohe.columns\n",
    "df_test_ohe_col = df_test_ohe_col.drop(['profit'])\n",
    "df_test_ohe_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вычисляем и визуализализируем `feature importance` для тестового датасета"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>babies</th>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <th>previous_cancellations</th>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>total_nights</th>\n",
       "      <th>winter_night_cost</th>\n",
       "      <th>one_time_cleaning_winter</th>\n",
       "      <th>season_coeff</th>\n",
       "      <th>arrival_date_month_August</th>\n",
       "      <th>arrival_date_month_December</th>\n",
       "      <th>arrival_date_month_February</th>\n",
       "      <th>arrival_date_month_January</th>\n",
       "      <th>arrival_date_month_July</th>\n",
       "      <th>arrival_date_month_June</th>\n",
       "      <th>arrival_date_month_March</th>\n",
       "      <th>arrival_date_month_May</th>\n",
       "      <th>arrival_date_month_November</th>\n",
       "      <th>arrival_date_month_October</th>\n",
       "      <th>arrival_date_month_September</th>\n",
       "      <th>meal_FB</th>\n",
       "      <th>meal_HB</th>\n",
       "      <th>meal_SC</th>\n",
       "      <th>meal_SC</th>\n",
       "      <th>distribution_channel_Direct</th>\n",
       "      <th>distribution_channel_GDS</th>\n",
       "      <th>distribution_channel_TA/TO</th>\n",
       "      <th>reserved_room_type_B</th>\n",
       "      <th>reserved_room_type_C</th>\n",
       "      <th>reserved_room_type_D</th>\n",
       "      <th>reserved_room_type_E</th>\n",
       "      <th>reserved_room_type_F</th>\n",
       "      <th>reserved_room_type_G</th>\n",
       "      <th>customer_type_Group</th>\n",
       "      <th>customer_type_Transient</th>\n",
       "      <th>customer_type_Transient-Party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_canceled  lead_time  arrival_date_year  arrival_date_day_of_month  \\\n",
       "0            1         74               2017                          1   \n",
       "1            1         62               2017                          1   \n",
       "2            1         62               2017                          1   \n",
       "3            1         71               2017                          1   \n",
       "4            1        172               2017                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  children  babies  \\\n",
       "0                        1                     0       2         0       0   \n",
       "1                        2                     2       2         0       0   \n",
       "2                        2                     2       2         0       0   \n",
       "3                        2                     2       1         0       0   \n",
       "4                        2                     5       2         0       0   \n",
       "\n",
       "   is_repeated_guest  previous_cancellations  previous_bookings_not_canceled  \\\n",
       "0                  0                       0                               0   \n",
       "1                  0                       0                               0   \n",
       "2                  0                       0                               0   \n",
       "3                  0                       0                               0   \n",
       "4                  0                       0                               0   \n",
       "\n",
       "   booking_changes  days_in_waiting_list  required_car_parking_spaces  \\\n",
       "0                0                     0                            0   \n",
       "1                0                     0                            0   \n",
       "2                0                     0                            0   \n",
       "3                0                     0                            0   \n",
       "4                0                     0                            0   \n",
       "\n",
       "   total_of_special_requests  total_nights  winter_night_cost  \\\n",
       "0                          0             1               1000   \n",
       "1                          1             4               1000   \n",
       "2                          1             4                600   \n",
       "3                          1             4                600   \n",
       "4                          0             7               1000   \n",
       "\n",
       "   one_time_cleaning_winter  season_coeff  arrival_date_month_August  \\\n",
       "0                       400           1.0                          0   \n",
       "1                       400           1.0                          0   \n",
       "2                       350           1.0                          0   \n",
       "3                       350           1.0                          0   \n",
       "4                       400           1.0                          0   \n",
       "\n",
       "   arrival_date_month_December  arrival_date_month_February  \\\n",
       "0                            0                            0   \n",
       "1                            0                            0   \n",
       "2                            0                            0   \n",
       "3                            0                            0   \n",
       "4                            0                            0   \n",
       "\n",
       "   arrival_date_month_January  arrival_date_month_July  \\\n",
       "0                           1                        0   \n",
       "1                           1                        0   \n",
       "2                           1                        0   \n",
       "3                           1                        0   \n",
       "4                           1                        0   \n",
       "\n",
       "   arrival_date_month_June  arrival_date_month_March  arrival_date_month_May  \\\n",
       "0                        0                         0                       0   \n",
       "1                        0                         0                       0   \n",
       "2                        0                         0                       0   \n",
       "3                        0                         0                       0   \n",
       "4                        0                         0                       0   \n",
       "\n",
       "   arrival_date_month_November  arrival_date_month_October  \\\n",
       "0                            0                           0   \n",
       "1                            0                           0   \n",
       "2                            0                           0   \n",
       "3                            0                           0   \n",
       "4                            0                           0   \n",
       "\n",
       "   arrival_date_month_September  meal_FB         meal_HB         meal_SC  \\\n",
       "0                             0               0               0        0   \n",
       "1                             0               0               0        0   \n",
       "2                             0               0               0        0   \n",
       "3                             0               0               0        0   \n",
       "4                             0               0               0        0   \n",
       "\n",
       "   meal_SC         distribution_channel_Direct  distribution_channel_GDS  \\\n",
       "0               0                            0                         0   \n",
       "1               0                            0                         0   \n",
       "2               0                            0                         0   \n",
       "3               0                            0                         0   \n",
       "4               0                            0                         0   \n",
       "\n",
       "   distribution_channel_TA/TO  reserved_room_type_B                 \\\n",
       "0                           1                                    0   \n",
       "1                           1                                    0   \n",
       "2                           1                                    0   \n",
       "3                           1                                    0   \n",
       "4                           1                                    0   \n",
       "\n",
       "   reserved_room_type_C                 reserved_room_type_D                 \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   reserved_room_type_E                 reserved_room_type_F                 \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   reserved_room_type_G                 customer_type_Group  \\\n",
       "0                                    0                    0   \n",
       "1                                    0                    0   \n",
       "2                                    0                    0   \n",
       "3                                    0                    0   \n",
       "4                                    0                    0   \n",
       "\n",
       "   customer_type_Transient  customer_type_Transient-Party  \n",
       "0                        1                              0  \n",
       "1                        1                              0  \n",
       "2                        1                              0  \n",
       "3                        1                              0  \n",
       "4                        1                              0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_test_v = pd.DataFrame(data=df_test_ohe, columns=df_test_ohe_col)\n",
    "df_test_v['is_canceled'] = df_test_ohe['is_canceled']\n",
    "display(df_test_v.head())\n",
    "\n",
    "model = DecisionTreeClassifier(random_state=12345, max_depth = 4)\n",
    "model.fit(X = df_test_v.drop(['is_canceled'], axis=1),\n",
    "y = df_test_v['is_canceled'])\n",
    "\n",
    "df_test_imp = pd.Series(model.feature_importances_, df_test_v.drop(['is_canceled'], axis=1).columns)\n",
    "fig, ax = plt.subplots(figsize=(16,14))\n",
    "\n",
    "df_test_imp.plot.bar(ax=ax)\n",
    "ax.set_title(\"Важность признаков\")\n",
    "ax.set_xlabel('Признаки')\n",
    "ax.set_ylabel('Важность')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Вывод:` наибольшее влиеяние в порядке убывания, оказали параметры: \n",
    "\n",
    "*lead_time* \n",
    "\n",
    "*customer_type_Transient* \n",
    "\n",
    "*total_of_special_requests* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим, как именно эти значения оказывают влияние на отказ от бронирования."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Проверяем `lead_time`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# создаём список вместо array\n",
    "lead_time_list = df_test_ohe['lead_time'].unique()\n",
    "lead_time_list = pd.Series(lead_time_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проходимся циклом по всем значения `lead_time` чтобы понять при каких его значениях наблюдается наибольшее количество отмен"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Большинство отмен:                      177\n",
      "Наблюдается для  \"Lead time в днях\":    56\n"
     ]
    }
   ],
   "source": [
    "most_cancellations = 0\n",
    "best_lt = 0\n",
    "\n",
    "for i in lead_time_list:\n",
    "    cancellations = df_test_ohe.loc[(df_test_ohe['lead_time'] == i) & (df_test_ohe['is_canceled'] == 1), 'is_canceled'].count()\n",
    "    \n",
    "    if cancellations > most_cancellations:\n",
    "        most_cancellations = cancellations\n",
    "        best_lt = i\n",
    "print('Большинство отмен:                     ', most_cancellations)\n",
    "print('Наблюдается для  \"Lead time в днях\":   ', best_lt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создаём график для наглядности"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Статус отмены (1 - отмена, 0 - нет отмены')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df_test_ohe['lead_time'], df_test_ohe['is_canceled']).plot(kind = 'bar')\n",
    "plt.title('Отмены бронирований')\n",
    "plt.xlabel('Отмены')\n",
    "plt.ylabel('Статус отмены (1 - отмена, 0 - нет отмены')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Вывод:` Большинство отмен наблюдаются для отметки 56 дней между датой бронирования и датой прибытия"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Проверяем `total_of_special_requests`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# уникальные значения\n",
    "df_test_ohe['total_of_special_requests'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8575"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# находим количество отмен для значения ['total_of_special_requests'] == 0\n",
    "df_test_ohe.loc[(df_test_ohe['total_of_special_requests'] == 0) & (df_test_ohe['is_canceled'] == 1), 'is_canceled'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создаём график для наглядности"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Статус отмены (1 - отмена, 0 - нет отмены')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df_test_ohe['total_of_special_requests'], df_test_ohe['is_canceled']).plot(kind = 'bar')\n",
    "plt.title('Отмены бронирований')\n",
    "plt.xlabel('Спец отметки')\n",
    "plt.ylabel('Статус отмены (1 - отмена, 0 - нет отмены')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Вывод:` Большинство отмен наблюдаются для спец-отметки - 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Проверяем `customer_type_Transient`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=uint8)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# уникальные значения\n",
    "df_test_ohe['customer_type_Transient'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11805"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# находим количество отмен для значения ['customer_type_Transient'] == 1\n",
    "df_test_ohe.loc[(df_test_ohe['customer_type_Transient'] == 1) & (df_test_ohe['is_canceled'] == 1), 'is_canceled'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создаём график для наглядности"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Статус отмены (1 - отмена, 0 - нет отмены')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df_test_ohe['customer_type_Transient'], df_test_ohe['is_canceled']).plot(kind = 'bar')\n",
    "plt.title('Отмены бронирований')\n",
    "plt.xlabel('Тип заказчика - \"не связано с договором или групповым заездом\"')\n",
    "plt.ylabel('Статус отмены (1 - отмена, 0 - нет отмены')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Вывод:` Большинство отмен наблюдаются для типа заказчика - \"не связано с договором или групповым заездом\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Общий вывод, портрет ненадёжного клиента:`\n",
    "\n",
    "По результатам предсказаний модели, самое большое влияние, с большим отрываом, на целевой признак (отказ от бронирования) оказало `lead_time` — количество дней между датой бронирования и датой. \n",
    "\n",
    "На втором месте идёт день месяца - `customer_type_Transient` — тип заказчика. и с небольшим отвывом `total_of_special_requests` — количество специальных отметок.\n",
    "\n",
    "Портрет ненадёжного клиента склонного к отмене бронирования номера:\n",
    "\n",
    "- Клиент у которого между датой бронирования и датой прибытия прошло 56 дней \n",
    "- Клиенты со спец-отметкой - 0.\n",
    "- Клиенты относящиеся к типу заказчика - \"не связано с договором или групповым заездом\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1.7. <a id='toc1_1_7_'></a>[Шаг 6. Напишите общий вывод](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Резюме:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В результате проведённого исследования, наилучшей моделью для предсказания отмены бронирования окакзалась модель Дерева решений тк показала лучшую метрику полноты которая является ключевой в данной задаче.\n",
    "\n",
    "Самой важной метрикой для задачи является `полнота (Recall)` тк эта метрика определяет количество истинно положительных среди всех меток класса, которые были определены как «положительный». Эта оценочная метрика особенно важна когда в поставленной задаче ошибка нераспознания положительного класса высока. В нашем случае, нераспознание положительного класса приведёт неверной к оценке отказавшихся от бронирования постояльцев и как результат неверный прогноз отказников. Это приведёт к непредвиденным убыткам.\n",
    "\n",
    "В нашем кейсе наибольшую эффективность и точность показала модель:\n",
    "\n",
    "`DecisionTreeClassifier(max_depth=19, random_state=12345)`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Рекомендации:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "При выборе потенциальных постояльцев с которых стоит брать депозитную предоплату считаю целесообразным обратить внимание на следущие категории постояльцев:\n",
    "\n",
    "\n",
    "По результатам предсказаний модели, самое большое влияние, с большим отрываом, на целевой признак (отказ от бронирования) оказало `lead_time` — количество дней между датой бронирования и датой. \n",
    "\n",
    "На втором месте идёт день месяца - `customer_type_Transient` — тип заказчика и с небольшим отвывом `total_of_special_requests` — количество специальных отметок.\n",
    "\n",
    "Следует избегать клиентов близких к портрету ненедёжного клиента, которым является следующий набор характеристик:\n",
    "\n",
    "\n",
    "- Клиент у которого между датой бронирования и датой прибытия прошло 56 дней \n",
    "- Клиенты со спец-отметкой - 0.\n",
    "- Клиенты относящиеся к типу заказчика - \"не связано с договором или групповым заездом\""
   ]
  }
 ],
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