Hotel Cancellation - Data Analysis - End to End Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ed184fb8",
   "metadata": {},
   "source": [
    "## Hotel Cancelation Data Analysis Project - Muhammad Abrar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c190649c",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: seaborn\n",
      "Successfully installed seaborn-0.12.2\n"
     ]
    }
   ],
   "source": [
    "import sys  \n",
    "!{sys.executable} -m pip install seaborn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ededc0c7",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e26bd02",
   "metadata": {},
   "source": [
    "# Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3424e92c",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('Python_Project_09_Random_Forest_Classification_Prospect_Hotel_Booking_Cancellation.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6b1b76d",
   "metadata": {},
   "source": [
    "\n",
    "# Exploratory Data Analysis and Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4d764343",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
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       "      <th>hotel</th>\n",
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       "      <th>...</th>\n",
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       "      <th>agent</th>\n",
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       "      <th>days_in_waiting_list</th>\n",
       "      <th>customer_type</th>\n",
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       "      <td>Check-Out</td>\n",
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       "      <td>Resort Hotel</td>\n",
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       "      <td>Resort Hotel</td>\n",
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       "      <td>2015</td>\n",
       "      <td>July</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Resort Hotel</td>\n",
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       "      <td>Resort Hotel</td>\n",
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       "      <td>Resort Hotel</td>\n",
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       "      <td>2015</td>\n",
       "      <td>July</td>\n",
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       "      <td>0</td>\n",
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       "      <td>No Deposit</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Transient</td>\n",
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       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>03/07/2015</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Resort Hotel</td>\n",
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       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>No Deposit</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Canceled</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
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       "      <td>No Deposit</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Transient</td>\n",
       "      <td>105.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Canceled</td>\n",
       "      <td>22/04/2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          hotel  is_canceled  lead_time  arrival_date_year arrival_date_month   \n",
       "0  Resort Hotel            0        342               2015               July  \\\n",
       "1  Resort Hotel            0        737               2015               July   \n",
       "2  Resort Hotel            0          7               2015               July   \n",
       "3  Resort Hotel            0         13               2015               July   \n",
       "4  Resort Hotel            0         14               2015               July   \n",
       "5  Resort Hotel            0         14               2015               July   \n",
       "6  Resort Hotel            0          0               2015               July   \n",
       "7  Resort Hotel            0          9               2015               July   \n",
       "8  Resort Hotel            1         85               2015               July   \n",
       "9  Resort Hotel            1         75               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",
       "5                        27                          1   \n",
       "6                        27                          1   \n",
       "7                        27                          1   \n",
       "8                        27                          1   \n",
       "9                        27                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  ...  deposit_type   \n",
       "0                        0                     0       2  ...    No Deposit  \\\n",
       "1                        0                     0       2  ...    No Deposit   \n",
       "2                        0                     1       1  ...    No Deposit   \n",
       "3                        0                     1       1  ...    No Deposit   \n",
       "4                        0                     2       2  ...    No Deposit   \n",
       "5                        0                     2       2  ...    No Deposit   \n",
       "6                        0                     2       2  ...    No Deposit   \n",
       "7                        0                     2       2  ...    No Deposit   \n",
       "8                        0                     3       2  ...    No Deposit   \n",
       "9                        0                     3       2  ...    No Deposit   \n",
       "\n",
       "   agent company days_in_waiting_list customer_type    adr   \n",
       "0    NaN     NaN                    0     Transient    0.0  \\\n",
       "1    NaN     NaN                    0     Transient    0.0   \n",
       "2    NaN     NaN                    0     Transient   75.0   \n",
       "3  304.0     NaN                    0     Transient   75.0   \n",
       "4  240.0     NaN                    0     Transient   98.0   \n",
       "5  240.0     NaN                    0     Transient   98.0   \n",
       "6    NaN     NaN                    0     Transient  107.0   \n",
       "7  303.0     NaN                    0     Transient  103.0   \n",
       "8  240.0     NaN                    0     Transient   82.0   \n",
       "9   15.0     NaN                    0     Transient  105.5   \n",
       "\n",
       "   required_car_parking_spaces  total_of_special_requests  reservation_status   \n",
       "0                            0                          0           Check-Out  \\\n",
       "1                            0                          0           Check-Out   \n",
       "2                            0                          0           Check-Out   \n",
       "3                            0                          0           Check-Out   \n",
       "4                            0                          1           Check-Out   \n",
       "5                            0                          1           Check-Out   \n",
       "6                            0                          0           Check-Out   \n",
       "7                            0                          1           Check-Out   \n",
       "8                            0                          1            Canceled   \n",
       "9                            0                          0            Canceled   \n",
       "\n",
       "  reservation_status_date  \n",
       "0              01/07/2015  \n",
       "1              01/07/2015  \n",
       "2              02/07/2015  \n",
       "3              02/07/2015  \n",
       "4              03/07/2015  \n",
       "5              03/07/2015  \n",
       "6              03/07/2015  \n",
       "7              03/07/2015  \n",
       "8              06/05/2015  \n",
       "9              22/04/2015  \n",
       "\n",
       "[10 rows x 32 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4e365c6b",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\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>hotel</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>deposit_type</th>\n",
       "      <th>agent</th>\n",
       "      <th>company</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>adr</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>reservation_status</th>\n",
       "      <th>reservation_status_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>119385</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>2017</td>\n",
       "      <td>August</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>394.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>96.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>06/09/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119386</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>2017</td>\n",
       "      <td>August</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>225.43</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>07/09/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119387</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>2017</td>\n",
       "      <td>August</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>157.71</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>07/09/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119388</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>2017</td>\n",
       "      <td>August</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>104.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>07/09/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119389</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>205</td>\n",
       "      <td>2017</td>\n",
       "      <td>August</td>\n",
       "      <td>35</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>151.20</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>07/09/2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             hotel  is_canceled  lead_time  arrival_date_year   \n",
       "119385  City Hotel            0         23               2017  \\\n",
       "119386  City Hotel            0        102               2017   \n",
       "119387  City Hotel            0         34               2017   \n",
       "119388  City Hotel            0        109               2017   \n",
       "119389  City Hotel            0        205               2017   \n",
       "\n",
       "       arrival_date_month  arrival_date_week_number   \n",
       "119385             August                        35  \\\n",
       "119386             August                        35   \n",
       "119387             August                        35   \n",
       "119388             August                        35   \n",
       "119389             August                        35   \n",
       "\n",
       "        arrival_date_day_of_month  stays_in_weekend_nights   \n",
       "119385                         30                        2  \\\n",
       "119386                         31                        2   \n",
       "119387                         31                        2   \n",
       "119388                         31                        2   \n",
       "119389                         29                        2   \n",
       "\n",
       "        stays_in_week_nights  adults  ...  deposit_type  agent company   \n",
       "119385                     5       2  ...    No Deposit  394.0     NaN  \\\n",
       "119386                     5       3  ...    No Deposit    9.0     NaN   \n",
       "119387                     5       2  ...    No Deposit    9.0     NaN   \n",
       "119388                     5       2  ...    No Deposit   89.0     NaN   \n",
       "119389                     7       2  ...    No Deposit    9.0     NaN   \n",
       "\n",
       "       days_in_waiting_list customer_type     adr   \n",
       "119385                    0     Transient   96.14  \\\n",
       "119386                    0     Transient  225.43   \n",
       "119387                    0     Transient  157.71   \n",
       "119388                    0     Transient  104.40   \n",
       "119389                    0     Transient  151.20   \n",
       "\n",
       "        required_car_parking_spaces  total_of_special_requests   \n",
       "119385                            0                          0  \\\n",
       "119386                            0                          2   \n",
       "119387                            0                          4   \n",
       "119388                            0                          0   \n",
       "119389                            0                          2   \n",
       "\n",
       "        reservation_status reservation_status_date  \n",
       "119385           Check-Out              06/09/2017  \n",
       "119386           Check-Out              07/09/2017  \n",
       "119387           Check-Out              07/09/2017  \n",
       "119388           Check-Out              07/09/2017  \n",
       "119389           Check-Out              07/09/2017  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d6bdfadb",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(119390, 32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea9c3d45",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',\n",
       "       'arrival_date_month', 'arrival_date_week_number',\n",
       "       'arrival_date_day_of_month', 'stays_in_weekend_nights',\n",
       "       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',\n",
       "       'country', 'market_segment', 'distribution_channel',\n",
       "       'is_repeated_guest', 'previous_cancellations',\n",
       "       'previous_bookings_not_canceled', 'reserved_room_type',\n",
       "       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',\n",
       "       'company', 'days_in_waiting_list', 'customer_type', 'adr',\n",
       "       'required_car_parking_spaces', 'total_of_special_requests',\n",
       "       'reservation_status', 'reservation_status_date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8490521d",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 119390 entries, 0 to 119389\n",
      "Data columns (total 32 columns):\n",
      " #   Column                          Non-Null Count   Dtype  \n",
      "---  ------                          --------------   -----  \n",
      " 0   hotel                           119390 non-null  object \n",
      " 1   is_canceled                     119390 non-null  int64  \n",
      " 2   lead_time                       119390 non-null  int64  \n",
      " 3   arrival_date_year               119390 non-null  int64  \n",
      " 4   arrival_date_month              119390 non-null  object \n",
      " 5   arrival_date_week_number        119390 non-null  int64  \n",
      " 6   arrival_date_day_of_month       119390 non-null  int64  \n",
      " 7   stays_in_weekend_nights         119390 non-null  int64  \n",
      " 8   stays_in_week_nights            119390 non-null  int64  \n",
      " 9   adults                          119390 non-null  int64  \n",
      " 10  children                        119386 non-null  float64\n",
      " 11  babies                          119390 non-null  int64  \n",
      " 12  meal                            119390 non-null  object \n",
      " 13  country                         118902 non-null  object \n",
      " 14  market_segment                  119390 non-null  object \n",
      " 15  distribution_channel            119390 non-null  object \n",
      " 16  is_repeated_guest               119390 non-null  int64  \n",
      " 17  previous_cancellations          119390 non-null  int64  \n",
      " 18  previous_bookings_not_canceled  119390 non-null  int64  \n",
      " 19  reserved_room_type              119390 non-null  object \n",
      " 20  assigned_room_type              119390 non-null  object \n",
      " 21  booking_changes                 119390 non-null  int64  \n",
      " 22  deposit_type                    119390 non-null  object \n",
      " 23  agent                           103050 non-null  float64\n",
      " 24  company                         6797 non-null    float64\n",
      " 25  days_in_waiting_list            119390 non-null  int64  \n",
      " 26  customer_type                   119390 non-null  object \n",
      " 27  adr                             119390 non-null  float64\n",
      " 28  required_car_parking_spaces     119390 non-null  int64  \n",
      " 29  total_of_special_requests       119390 non-null  int64  \n",
      " 30  reservation_status              119390 non-null  object \n",
      " 31  reservation_status_date         119390 non-null  object \n",
      "dtypes: float64(4), int64(16), object(12)\n",
      "memory usage: 29.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3d79c61",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "643ff453",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Reservation_status_date (30) is an object, so converting it into date_time type   \n",
    "\n",
    "df['reservation_status_date'] = pd.to_datetime(df['reservation_status_date'], format= 'mixed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f859a6dc",
   "metadata": {
    "collapsed": true
   },
   "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>hotel</th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>meal</th>\n",
       "      <th>country</th>\n",
       "      <th>market_segment</th>\n",
       "      <th>distribution_channel</th>\n",
       "      <th>reserved_room_type</th>\n",
       "      <th>assigned_room_type</th>\n",
       "      <th>deposit_type</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>reservation_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>119390</td>\n",
       "      <td>119390</td>\n",
       "      <td>119390</td>\n",
       "      <td>118902</td>\n",
       "      <td>119390</td>\n",
       "      <td>119390</td>\n",
       "      <td>119390</td>\n",
       "      <td>119390</td>\n",
       "      <td>119390</td>\n",
       "      <td>119390</td>\n",
       "      <td>119390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>177</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>August</td>\n",
       "      <td>BB</td>\n",
       "      <td>PRT</td>\n",
       "      <td>Online TA</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>Transient</td>\n",
       "      <td>Check-Out</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>79330</td>\n",
       "      <td>13877</td>\n",
       "      <td>92310</td>\n",
       "      <td>48590</td>\n",
       "      <td>56477</td>\n",
       "      <td>97870</td>\n",
       "      <td>85994</td>\n",
       "      <td>74053</td>\n",
       "      <td>104641</td>\n",
       "      <td>89613</td>\n",
       "      <td>75166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             hotel arrival_date_month    meal country market_segment   \n",
       "count       119390             119390  119390  118902         119390  \\\n",
       "unique           2                 12       5     177              8   \n",
       "top     City Hotel             August      BB     PRT      Online TA   \n",
       "freq         79330              13877   92310   48590          56477   \n",
       "\n",
       "       distribution_channel reserved_room_type assigned_room_type   \n",
       "count                119390             119390             119390  \\\n",
       "unique                    5                 10                 12   \n",
       "top                   TA/TO                  A                  A   \n",
       "freq                  97870              85994              74053   \n",
       "\n",
       "       deposit_type customer_type reservation_status  \n",
       "count        119390        119390             119390  \n",
       "unique            3             4                  3  \n",
       "top      No Deposit     Transient          Check-Out  \n",
       "freq         104641         89613              75166  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To check summary statistics of objects (categorical data types) in the dataset\n",
    "\n",
    "\n",
    "df.describe(include = 'object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "838c57b0",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hotel\n",
      "['Resort Hotel' 'City Hotel']\n",
      "*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-\n",
      "arrival_date_month\n",
      "['July' 'August' 'September' 'October' 'November' 'December' 'January'\n",
      " 'February' 'March' 'April' 'May' 'June']\n",
      "*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-\n",
      "meal\n",
      "['BB' 'FB' 'HB' 'SC' 'Undefined']\n",
      "*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-\n",
      "country\n",
      "['PRT' 'GBR' 'USA' 'ESP' 'IRL' 'FRA' nan 'ROU' 'NOR' 'OMN' 'ARG' 'POL'\n",
      " 'DEU' 'BEL' 'CHE' 'CN' 'GRC' 'ITA' 'NLD' 'DNK' 'RUS' 'SWE' 'AUS' 'EST'\n",
      " 'CZE' 'BRA' 'FIN' 'MOZ' 'BWA' 'LUX' 'SVN' 'ALB' 'IND' 'CHN' 'MEX' 'MAR'\n",
      " 'UKR' 'SMR' 'LVA' 'PRI' 'SRB' 'CHL' 'AUT' 'BLR' 'LTU' 'TUR' 'ZAF' 'AGO'\n",
      " 'ISR' 'CYM' 'ZMB' 'CPV' 'ZWE' 'DZA' 'KOR' 'CRI' 'HUN' 'ARE' 'TUN' 'JAM'\n",
      " 'HRV' 'HKG' 'IRN' 'GEO' 'AND' 'GIB' 'URY' 'JEY' 'CAF' 'CYP' 'COL' 'GGY'\n",
      " 'KWT' 'NGA' 'MDV' 'VEN' 'SVK' 'FJI' 'KAZ' 'PAK' 'IDN' 'LBN' 'PHL' 'SEN'\n",
      " 'SYC' 'AZE' 'BHR' 'NZL' 'THA' 'DOM' 'MKD' 'MYS' 'ARM' 'JPN' 'LKA' 'CUB'\n",
      " 'CMR' 'BIH' 'MUS' 'COM' 'SUR' 'UGA' 'BGR' 'CIV' 'JOR' 'SYR' 'SGP' 'BDI'\n",
      " 'SAU' 'VNM' 'PLW' 'QAT' 'EGY' 'PER' 'MLT' 'MWI' 'ECU' 'MDG' 'ISL' 'UZB'\n",
      " 'NPL' 'BHS' 'MAC' 'TGO' 'TWN' 'DJI' 'STP' 'KNA' 'ETH' 'IRQ' 'HND' 'RWA'\n",
      " 'KHM' 'MCO' 'BGD' 'IMN' 'TJK' 'NIC' 'BEN' 'VGB' 'TZA' 'GAB' 'GHA' 'TMP'\n",
      " 'GLP' 'KEN' 'LIE' 'GNB' 'MNE' 'UMI' 'MYT' 'FRO' 'MMR' 'PAN' 'BFA' 'LBY'\n",
      " 'MLI' 'NAM' 'BOL' 'PRY' 'BRB' 'ABW' 'AIA' 'SLV' 'DMA' 'PYF' 'GUY' 'LCA'\n",
      " 'ATA' 'GTM' 'ASM' 'MRT' 'NCL' 'KIR' 'SDN' 'ATF' 'SLE' 'LAO']\n",
      "*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-\n",
      "market_segment\n",
      "['Direct' 'Corporate' 'Online TA' 'Offline TA/TO' 'Complementary' 'Groups'\n",
      " 'Undefined' 'Aviation']\n",
      "*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-\n",
      "distribution_channel\n",
      "['Direct' 'Corporate' 'TA/TO' 'Undefined' 'GDS']\n",
      "*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-\n",
      "reserved_room_type\n",
      "['C' 'A' 'D' 'E' 'G' 'F' 'H' 'L' 'P' 'B']\n",
      "*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-\n",
      "assigned_room_type\n",
      "['C' 'A' 'D' 'E' 'G' 'F' 'I' 'B' 'H' 'P' 'L' 'K']\n",
      "*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-\n",
      "deposit_type\n",
      "['No Deposit' 'Refundable' 'Non Refund']\n",
      "*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-\n",
      "customer_type\n",
      "['Transient' 'Contract' 'Transient-Party' 'Group']\n",
      "*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-\n",
      "reservation_status\n",
      "['Check-Out' 'Canceled' 'No-Show']\n",
      "*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-\n"
     ]
    }
   ],
   "source": [
    "for col in df.describe(include = 'object').columns:\n",
    "    print(col)\n",
    "    print(df[col].unique())\n",
    "    print('*-'*40)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9717a38c",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hotel                                  0\n",
       "is_canceled                            0\n",
       "lead_time                              0\n",
       "arrival_date_year                      0\n",
       "arrival_date_month                     0\n",
       "arrival_date_week_number               0\n",
       "arrival_date_day_of_month              0\n",
       "stays_in_weekend_nights                0\n",
       "stays_in_week_nights                   0\n",
       "adults                                 0\n",
       "children                               4\n",
       "babies                                 0\n",
       "meal                                   0\n",
       "country                              488\n",
       "market_segment                         0\n",
       "distribution_channel                   0\n",
       "is_repeated_guest                      0\n",
       "previous_cancellations                 0\n",
       "previous_bookings_not_canceled         0\n",
       "reserved_room_type                     0\n",
       "assigned_room_type                     0\n",
       "booking_changes                        0\n",
       "deposit_type                           0\n",
       "agent                              16340\n",
       "company                           112593\n",
       "days_in_waiting_list                   0\n",
       "customer_type                          0\n",
       "adr                                    0\n",
       "required_car_parking_spaces            0\n",
       "total_of_special_requests              0\n",
       "reservation_status                     0\n",
       "reservation_status_date                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking missing Values\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5cd64be",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Since company column has much null values we will drop it and we dont need agent column so we are droping it. for children and country dropping them wont make a significant change to the analysis so we will drop null rows.\n",
    "\n",
    "df.drop(['company','agent'],axis = 1, inplace=True)\n",
    "df.dropna(inplace = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8f3fb13b",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hotel                             0\n",
       "is_canceled                       0\n",
       "lead_time                         0\n",
       "arrival_date_year                 0\n",
       "arrival_date_month                0\n",
       "arrival_date_week_number          0\n",
       "arrival_date_day_of_month         0\n",
       "stays_in_weekend_nights           0\n",
       "stays_in_week_nights              0\n",
       "adults                            0\n",
       "children                          0\n",
       "babies                            0\n",
       "meal                              0\n",
       "country                           0\n",
       "market_segment                    0\n",
       "distribution_channel              0\n",
       "is_repeated_guest                 0\n",
       "previous_cancellations            0\n",
       "previous_bookings_not_canceled    0\n",
       "reserved_room_type                0\n",
       "assigned_room_type                0\n",
       "booking_changes                   0\n",
       "deposit_type                      0\n",
       "days_in_waiting_list              0\n",
       "customer_type                     0\n",
       "adr                               0\n",
       "required_car_parking_spaces       0\n",
       "total_of_special_requests         0\n",
       "reservation_status                0\n",
       "reservation_status_date           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "778b1560",
   "metadata": {
    "collapsed": true
   },
   "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_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>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>adr</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>reservation_status_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>118898.000000</td>\n",
       "      <td>118898.000000</td>\n",
       "      <td>118898.000000</td>\n",
       "      <td>118898.000000</td>\n",
       "      <td>118898.000000</td>\n",
       "      <td>118898.000000</td>\n",
       "      <td>118898.000000</td>\n",
       "      <td>118898.000000</td>\n",
       "      <td>118898.000000</td>\n",
       "      <td>118898.000000</td>\n",
       "      <td>118898.000000</td>\n",
       "      <td>118898.000000</td>\n",
       "      <td>118898.000000</td>\n",
       "      <td>118898.000000</td>\n",
       "      <td>118898.000000</td>\n",
       "      <td>118898.000000</td>\n",
       "      <td>118898.000000</td>\n",
       "      <td>118898.000000</td>\n",
       "      <td>118898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.371352</td>\n",
       "      <td>104.311435</td>\n",
       "      <td>2016.157656</td>\n",
       "      <td>27.166555</td>\n",
       "      <td>15.800880</td>\n",
       "      <td>0.928897</td>\n",
       "      <td>2.502145</td>\n",
       "      <td>1.858391</td>\n",
       "      <td>0.104207</td>\n",
       "      <td>0.007948</td>\n",
       "      <td>0.032011</td>\n",
       "      <td>0.087142</td>\n",
       "      <td>0.131634</td>\n",
       "      <td>0.221181</td>\n",
       "      <td>2.330754</td>\n",
       "      <td>102.003243</td>\n",
       "      <td>0.061885</td>\n",
       "      <td>0.571683</td>\n",
       "      <td>2016-07-31 20:49:36.695991552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>-6.380000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2014-10-17 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>16.000000</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>70.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2016-02-03 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>28.000000</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>95.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2016-08-05 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>38.000000</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>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2017-02-16 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>737.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>5400.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2017-12-09 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.483168</td>\n",
       "      <td>106.903309</td>\n",
       "      <td>0.707459</td>\n",
       "      <td>13.589971</td>\n",
       "      <td>8.780324</td>\n",
       "      <td>0.996216</td>\n",
       "      <td>1.900168</td>\n",
       "      <td>0.578576</td>\n",
       "      <td>0.399172</td>\n",
       "      <td>0.097380</td>\n",
       "      <td>0.176029</td>\n",
       "      <td>0.845869</td>\n",
       "      <td>1.484672</td>\n",
       "      <td>0.652785</td>\n",
       "      <td>17.630452</td>\n",
       "      <td>50.485862</td>\n",
       "      <td>0.244172</td>\n",
       "      <td>0.792678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         is_canceled      lead_time  arrival_date_year   \n",
       "count  118898.000000  118898.000000      118898.000000  \\\n",
       "mean        0.371352     104.311435        2016.157656   \n",
       "min         0.000000       0.000000        2015.000000   \n",
       "25%         0.000000      18.000000        2016.000000   \n",
       "50%         0.000000      69.000000        2016.000000   \n",
       "75%         1.000000     161.000000        2017.000000   \n",
       "max         1.000000     737.000000        2017.000000   \n",
       "std         0.483168     106.903309           0.707459   \n",
       "\n",
       "       arrival_date_week_number  arrival_date_day_of_month   \n",
       "count             118898.000000              118898.000000  \\\n",
       "mean                  27.166555                  15.800880   \n",
       "min                    1.000000                   1.000000   \n",
       "25%                   16.000000                   8.000000   \n",
       "50%                   28.000000                  16.000000   \n",
       "75%                   38.000000                  23.000000   \n",
       "max                   53.000000                  31.000000   \n",
       "std                   13.589971                   8.780324   \n",
       "\n",
       "       stays_in_weekend_nights  stays_in_week_nights         adults   \n",
       "count            118898.000000         118898.000000  118898.000000  \\\n",
       "mean                  0.928897              2.502145       1.858391   \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                  16.000000             41.000000      55.000000   \n",
       "std                   0.996216              1.900168       0.578576   \n",
       "\n",
       "            children         babies  is_repeated_guest   \n",
       "count  118898.000000  118898.000000      118898.000000  \\\n",
       "mean        0.104207       0.007948           0.032011   \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        10.000000      10.000000           1.000000   \n",
       "std         0.399172       0.097380           0.176029   \n",
       "\n",
       "       previous_cancellations  previous_bookings_not_canceled   \n",
       "count           118898.000000                   118898.000000  \\\n",
       "mean                 0.087142                        0.131634   \n",
       "min                  0.000000                        0.000000   \n",
       "25%                  0.000000                        0.000000   \n",
       "50%                  0.000000                        0.000000   \n",
       "75%                  0.000000                        0.000000   \n",
       "max                 26.000000                       72.000000   \n",
       "std                  0.845869                        1.484672   \n",
       "\n",
       "       booking_changes  days_in_waiting_list            adr   \n",
       "count    118898.000000         118898.000000  118898.000000  \\\n",
       "mean          0.221181              2.330754     102.003243   \n",
       "min           0.000000              0.000000      -6.380000   \n",
       "25%           0.000000              0.000000      70.000000   \n",
       "50%           0.000000              0.000000      95.000000   \n",
       "75%           0.000000              0.000000     126.000000   \n",
       "max          21.000000            391.000000    5400.000000   \n",
       "std           0.652785             17.630452      50.485862   \n",
       "\n",
       "       required_car_parking_spaces  total_of_special_requests   \n",
       "count                118898.000000              118898.000000  \\\n",
       "mean                      0.061885                   0.571683   \n",
       "min                       0.000000                   0.000000   \n",
       "25%                       0.000000                   0.000000   \n",
       "50%                       0.000000                   0.000000   \n",
       "75%                       0.000000                   1.000000   \n",
       "max                       8.000000                   5.000000   \n",
       "std                       0.244172                   0.792678   \n",
       "\n",
       "             reservation_status_date  \n",
       "count                         118898  \n",
       "mean   2016-07-31 20:49:36.695991552  \n",
       "min              2014-10-17 00:00:00  \n",
       "25%              2016-02-03 00:00:00  \n",
       "50%              2016-08-05 00:00:00  \n",
       "75%              2017-02-16 00:00:00  \n",
       "max              2017-12-09 00:00:00  \n",
       "std                              NaN  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#summary statistics of numerical column to check outliers\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b9123261",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['adr'].plot(kind = 'box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "57efb188",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#we have to remove the outlier that we just spotted  and can be seen in the above box plot\n",
    "df=df[df['adr']<5000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b5351e4f",
   "metadata": {
    "collapsed": true
   },
   "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_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>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>adr</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>reservation_status_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>118897.000000</td>\n",
       "      <td>118897.000000</td>\n",
       "      <td>118897.000000</td>\n",
       "      <td>118897.000000</td>\n",
       "      <td>118897.000000</td>\n",
       "      <td>118897.000000</td>\n",
       "      <td>118897.000000</td>\n",
       "      <td>118897.000000</td>\n",
       "      <td>118897.000000</td>\n",
       "      <td>118897.000000</td>\n",
       "      <td>118897.000000</td>\n",
       "      <td>118897.000000</td>\n",
       "      <td>118897.000000</td>\n",
       "      <td>118897.000000</td>\n",
       "      <td>118897.000000</td>\n",
       "      <td>118897.000000</td>\n",
       "      <td>118897.000000</td>\n",
       "      <td>118897.000000</td>\n",
       "      <td>118897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.371347</td>\n",
       "      <td>104.312018</td>\n",
       "      <td>2016.157657</td>\n",
       "      <td>27.166674</td>\n",
       "      <td>15.800802</td>\n",
       "      <td>0.928905</td>\n",
       "      <td>2.502157</td>\n",
       "      <td>1.858390</td>\n",
       "      <td>0.104208</td>\n",
       "      <td>0.007948</td>\n",
       "      <td>0.032011</td>\n",
       "      <td>0.087143</td>\n",
       "      <td>0.131635</td>\n",
       "      <td>0.221175</td>\n",
       "      <td>2.330774</td>\n",
       "      <td>101.958683</td>\n",
       "      <td>0.061885</td>\n",
       "      <td>0.571688</td>\n",
       "      <td>2016-07-31 20:51:35.775334912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>-6.380000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2014-10-17 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>16.000000</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>70.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2016-02-03 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>28.000000</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>95.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2016-08-05 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>38.000000</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>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2017-02-16 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>737.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2017-12-09 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.483167</td>\n",
       "      <td>106.903570</td>\n",
       "      <td>0.707462</td>\n",
       "      <td>13.589966</td>\n",
       "      <td>8.780321</td>\n",
       "      <td>0.996217</td>\n",
       "      <td>1.900171</td>\n",
       "      <td>0.578578</td>\n",
       "      <td>0.399174</td>\n",
       "      <td>0.097381</td>\n",
       "      <td>0.176030</td>\n",
       "      <td>0.845872</td>\n",
       "      <td>1.484678</td>\n",
       "      <td>0.652784</td>\n",
       "      <td>17.630525</td>\n",
       "      <td>48.091199</td>\n",
       "      <td>0.244173</td>\n",
       "      <td>0.792680</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         is_canceled      lead_time  arrival_date_year   \n",
       "count  118897.000000  118897.000000      118897.000000  \\\n",
       "mean        0.371347     104.312018        2016.157657   \n",
       "min         0.000000       0.000000        2015.000000   \n",
       "25%         0.000000      18.000000        2016.000000   \n",
       "50%         0.000000      69.000000        2016.000000   \n",
       "75%         1.000000     161.000000        2017.000000   \n",
       "max         1.000000     737.000000        2017.000000   \n",
       "std         0.483167     106.903570           0.707462   \n",
       "\n",
       "       arrival_date_week_number  arrival_date_day_of_month   \n",
       "count             118897.000000              118897.000000  \\\n",
       "mean                  27.166674                  15.800802   \n",
       "min                    1.000000                   1.000000   \n",
       "25%                   16.000000                   8.000000   \n",
       "50%                   28.000000                  16.000000   \n",
       "75%                   38.000000                  23.000000   \n",
       "max                   53.000000                  31.000000   \n",
       "std                   13.589966                   8.780321   \n",
       "\n",
       "       stays_in_weekend_nights  stays_in_week_nights         adults   \n",
       "count            118897.000000         118897.000000  118897.000000  \\\n",
       "mean                  0.928905              2.502157       1.858390   \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                  16.000000             41.000000      55.000000   \n",
       "std                   0.996217              1.900171       0.578578   \n",
       "\n",
       "            children         babies  is_repeated_guest   \n",
       "count  118897.000000  118897.000000      118897.000000  \\\n",
       "mean        0.104208       0.007948           0.032011   \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        10.000000      10.000000           1.000000   \n",
       "std         0.399174       0.097381           0.176030   \n",
       "\n",
       "       previous_cancellations  previous_bookings_not_canceled   \n",
       "count           118897.000000                   118897.000000  \\\n",
       "mean                 0.087143                        0.131635   \n",
       "min                  0.000000                        0.000000   \n",
       "25%                  0.000000                        0.000000   \n",
       "50%                  0.000000                        0.000000   \n",
       "75%                  0.000000                        0.000000   \n",
       "max                 26.000000                       72.000000   \n",
       "std                  0.845872                        1.484678   \n",
       "\n",
       "       booking_changes  days_in_waiting_list            adr   \n",
       "count    118897.000000         118897.000000  118897.000000  \\\n",
       "mean          0.221175              2.330774     101.958683   \n",
       "min           0.000000              0.000000      -6.380000   \n",
       "25%           0.000000              0.000000      70.000000   \n",
       "50%           0.000000              0.000000      95.000000   \n",
       "75%           0.000000              0.000000     126.000000   \n",
       "max          21.000000            391.000000     510.000000   \n",
       "std           0.652784             17.630525      48.091199   \n",
       "\n",
       "       required_car_parking_spaces  total_of_special_requests   \n",
       "count                118897.000000              118897.000000  \\\n",
       "mean                      0.061885                   0.571688   \n",
       "min                       0.000000                   0.000000   \n",
       "25%                       0.000000                   0.000000   \n",
       "50%                       0.000000                   0.000000   \n",
       "75%                       0.000000                   1.000000   \n",
       "max                       8.000000                   5.000000   \n",
       "std                       0.244173                   0.792680   \n",
       "\n",
       "             reservation_status_date  \n",
       "count                         118897  \n",
       "mean   2016-07-31 20:51:35.775334912  \n",
       "min              2014-10-17 00:00:00  \n",
       "25%              2016-02-03 00:00:00  \n",
       "50%              2016-08-05 00:00:00  \n",
       "75%              2017-02-16 00:00:00  \n",
       "max              2017-12-09 00:00:00  \n",
       "std                              NaN  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fa90975",
   "metadata": {},
   "source": [
    "# Data Analysis and Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "dea109f0",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_canceled\n",
       "0    0.628653\n",
       "1    0.371347\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we want to see canceled and non canceled datareservations data  first.\n",
    "cancelled_perc = df['is_canceled'].value_counts(normalize = True)\n",
    "cancelled_perc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2e1be25c",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is_canceled\n",
      "0    0.628653\n",
      "1    0.371347\n",
      "Name: proportion, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(cancelled_perc)\n",
    "\n",
    "plt.figure(figsize = (5,4))\n",
    "plt.title('Reservation status count')\n",
    "plt.bar (['Not canceled', 'canceled'], df['is_canceled'].value_counts(),edgecolor='k', width = 0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e3ba22fe",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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s2FGunCAI+P777wEAX3/9NXr06KG0vvfeew9Lly4F8LLl999//xX3paWliT936tSp1Jj09PRgbm4ut23Xrl24e/cujIyM8Msvv8DExETpsaNHj0abNm0AQGW/XB0dHWzatKnUhVP09PQwZMgQAC9bdJ88eaK0nKzvbbVq1Uq0ngCAs7NzqTEAL1ux3NzcAKBCV+3Kz8/Hpk2bAABNmjRR2kdQ1qe6evXqAIDVq1errPO7775DkyZNSmxv0KAB+vfvDwA4d+7cK8Ure6988MEHKstZWVm9Uv0yjo6OKgdi1a5dG1OmTAEAHDhwQOtT7GVkZKCwsBCA6s8Q8PrXpix2dnb46aeflM5HPXnyZPFn2TcC6khLSxNbxHr37o1hw4aVKGNmZoYNGzaoXXdFuXv3Lnbv3g3g5WeneD/d4lq0aAF/f38Aqu9RALB48WLUqVNH6b6zZ8+K8ztv27atxLdYMj169BBnKirrfFu2bClx7wWATz/9FPb29uJ5K9OECROU9v21srKCr68vgJet2VlZWXL79+3bJ46PmT17tnifLa5u3bri36tnz57JfZOgrnXr1iErKwu1atXC2rVroaen/Mv+efPmoVatWpBKpfjll1/KXX95/35KJBK1vmF8mzFJJrUpDjqpWbMmPD09cf78eZiYmCAwMBA7d+4scdzVq1dx+/ZtAChzarjiH+DiX7Xa2dmJP6t7Mzpw4AAAoHPnznJf9ak6v6qveTt06FDm4g+yQXuFhYX4/fffS+x//vy5mNj279+/1MRdRhAEpKWl4ebNm7hy5Yr4kH39f/nyZZXHv46YmBhkZmYCeDn4rrRuFObm5hg8eDCAl6/5gwcPlJaTSCSldkEBgFatWgF4mdTJzqsO2Xtl9+7dePbsmdrHv6rs7GwkJSUhMTFRfH1kr6tsnzZVr14dBgYGAIBff/21XIOWKsqgQYNK/SezYcOGYpcAWfcedZw8eRJFRUUAICZCyrRp00bpP2raEBYWhqKiIpiYmKBnz54qy8ruUampqaUODDUwMMAnn3xSah2ye2LDhg3lBl2rOt/FixdLfc80a9YMzZs3V7pPIpGgRYsWAF7t9XwdqgZPy+4zgiCU+GzKBshJJBJ88cUXpdbxySefiF1eXmdQnez16N27t8pVa/X09ODh4QFA9d8oRcX/fr5tC7ZUFCbJpFFubm74+uuvlbasXbp0SfzZw8ND6Sh+2UP2xxGQ/++3Y8eOqFevHgBg4sSJaNOmDYKDg3H+/PlSZ3hQPP+RI0dUnlsikWDZsmUlzq2otD8GxbVt2xb169cHAKWzNRw4cAA5OTkAVN/Iw8LC0Lt3b1hYWMDOzk78oyZ7yPp4V+Ryt1euXBF/btu2rcqyxfcXP664GjVqiC3OyhRvxXz69Gl5wxT5+PgAACIjI+Hk5ITx48dj3759ePTokdp1leXevXuYMGECHB0dYWFhgXr16qFp06bi6zNmzBixrLaXJDY0NBS/4dizZw8aNGiAqVOn4tChQ6/0z8jraNSokcr9stasV3n9i/clb926tcqysm+OtE12j3r27Bn09PRU3qN69+4tHlfafcrZ2RlGRkZlnu/GjRtl3hPHjx8P4OU//BkZGUrrK+v1lH2mX+X1fB2q4lJ1n5Hdu5ycnFQ2rBgYGIj/AJR2vytLUVGROJ7l559/LvP12LNnDwDVf6MUOTk5id+srVixAk2aNMGcOXNw4sSJSm1IeJMwSSa1jRs3TlxKNS4uDgcPHoSPjw90dHQQGRmJLl26KE1E0tPTX+l8xT+8+vr6OHjwoDj46eLFi5g5cyY6duwIS0tL9OjRAzt37hRbkF73/KqmWSrv11Gy5DcyMrLEtEuyxFk21ZIiQRAwatQo9O7dG2FhYWX+cVEV7+sq/oexrEE0tra2So8rrqxWcx2d/397UvZ6luXbb7/FF198AYlEgvT0dKxZswYDBgwQF8OZO3euRqY5Onz4MFxcXLB69WpxqidVKvI1Kq/Vq1ejT58+AF4m+EuXLkWvXr1QvXp1tG7dGkuXLi3x1XNFKO974FVef3XerzY2NmrXXxE0cY8srqx7lKbPV5Gv5+tQFZeq+4zsPVSeQYOye15p97uyZGRkvNK3Ouomt7/99pvYCn316lV899138PT0FKfeXL9+PfLy8tSO423F2S1IbYor7rm5uaF3797o2rUrRo4cibt372LUqFElRscXvwEdPHiwzK4Kxc9XnIuLCxISEnDw4EEcPHgQZ86cwa1bt/D8+XMcOXIER44cwfLly3Ho0CG5Y2Xn79mzJ5YsWaLu0y6hvEtuDx8+HPPnz4cgCPjtt98wY8YMAC9virKZDIYMGaK0/9mWLVuwefNmAC+v88SJE9G2bVvUqlULJiYmYgwjRozAr7/+Wmn9XZX1Ia1q9PX1sXnzZkyaNAm//fYbTpw4gUuXLqGgoACJiYlITEzE8uXLsX37dqV9wcvj8ePH+PTTT/Hs2TNUq1YNkydPhre3N+rXrw8LCwuxW8OJEyfg6ekJAFrvkwy87BJz4MABREdH4/fff8epU6cQHx+PoqIiXLp0CZcuXcKyZcuwf/9+8Q/qm+xNeL8C//8eVaNGDZw8ebLcx5XWd7mse5TsfK6urti+fXu5z1d8dpd3QWW8f4r/fRw1ahS++eabch0nu8eUV61atRAZGYmIiAjs3bsXp0+fxtWrV1FYWIizZ8/i7NmzWLZsGQ4dOsSVIcEkmTTIx8cHBw8exJ9//okDBw7gxIkT6Natm7i/+FfrlpaWJZa2Voeuri769+8vDu568OABwsPDsWbNGsTExCAmJgZffvml3FQ21atXR2pqKgoKCl7r3Op6//334e7ujkuXLmHnzp1ikrxnzx6xi0hpXS02btwI4OVAtsjISBgbGyst96qtF+oo/rXkw4cPVd5Ai38FWNGDv8ri4uKC7777Dt999x3y8vJw7tw57Ny5E7/88gtycnIwbNgw3L59W66/Xnnt2bNH7KKwb98+pd8GAJp9fSQSCQRBULlgCwDk5uaWWVebNm3ErgZPnz7FqVOnEBISgr179yI9PR0DBw7E7du3S33fVWXFW1EfPnwIBweHUstWlYUTZPfIp0+fonHjxuX+R/x1z5eTk1Op98Q3hezeVZ73h+ye96r3u+LHCYJQ4a+Hp6en+I/7kydPcPz4cWzYsAEnTpzA7du3MWTIEHEg/ruM3S1IoxYtWiTe2GfOnCm3T9ZnC3g5a4Um2dnZwdfXF1FRUWjZsiUAIDQ0VO6rbdn5Za2JlUmWBF+5cgX//PMPgP/f1aJ+/fql9vFNTEwEAPTt27fUREUQBMTGxmo65BKK37QvXLigsmx0dLTS47TNyMgIXl5e2LJlizgi/fnz5wgNDZUrV96WI9nrY2VlVWqCDMj3x1dGnZYqMzMzAC9XYiyNIAi4detWueuU1dunTx/8+eef+PrrrwG8/OfzVWcX0bbiA9EuXryosmxZ+yuL7B6Vn59f5ntGk+e7c+eOWn1bK0JVbO2X3buSkpJUjmUoLCwUE0pl97vyPDcDAwNxAKmm/z6WpXr16hgyZAgiIiLQt29fAEB8fLzczFLvKibJpFHvv/++OLPBhQsXcOzYMXFfy5YtUbt2bQDAhg0bKqTfk76+Pjp37gzg5SIPxQciyT78WVlZrzVNz6sYOnSo+M/Djh07cP/+fXEaJFUD9mR91FS1Cv7111+lziAhIxu8k5+fr1bcxbVq1QqWlpYAXk4XVVpL5tOnT8WZPFxcXF6phbYyyFpRgJKD6YoPdlJ1zWSvT15eXqnX49mzZ/j1119VxqLO6yP7al1VEnX48OHXGoSn6tq8Kbp27Sp+5mTLfitz8eLFVx5spWl9+vQRE6qVK1dW+Plk90RBEPDjjz9W+PlUKe9nrjLJ/vEVBEHl34w9e/aIffiV/bNc3s+37PW4fv16pSwqpMzb8NnXJCbJpHEzZ84Ub/TFVxfS0dERW5fv3LmDESNGqLxpZGdnl5hn9+zZsypbyAoKCnD69GkAL+cdLj4i2cfHR/zKdfLkyThz5ozK53Hu3Dmxrtdla2srdj357bffsHPnTrFvqqokWTZH8sGDB5V+ZX/79m1xvlRVZImqbAq+V2FoaIhRo0YBeNki/t1335UoIwgCxo8fL95cZSPiK1tGRgYOHjyosv/v0aNHxZ8V+3QWT+xVXTPZ6/Ps2TOlU/wVFRVh1KhR4lyrpZGdLz09vczBmbJ/Ai9cuKC0xSktLQ0TJkwo9fg7d+6U+b5WdW3eFHZ2dmJf8wMHDih9fXJycpSusqYtDRs2FKds27VrV5mrqSUlJeG333575fN1795d7G6zdOlSpdeoONlYkIpQfGrC17lPaVL//v3FuZ0XLlyodPXNlJQUcU5vExMTpdMNlvf++80334gzO/n6+orfVJUmLCxM/GayPOLj48UZNJQRBEFu2rvyjht6m7FPMmlc06ZN0bdvX/z11184c+YMzp07Jy4sMnbsWBw7dgz79u3DH3/8gdjYWHz55Zdo06YNLCwskJ2djevXr+PUqVM4cOAAjIyM5BKtiIgIfPfdd/jggw/Qq1cvNG/eHNbW1nj+/Dlu3ryJ9evXi10P/Pz85AbDGRoa4vfff0eXLl2Qk5ODbt26YejQoejfvz+cnJwglUrx4MEDxMTEYN++fUhISMBPP/0kJiWva/jw4Th27BhSUlIQHBwMAHB3d1fZt3fEiBGYMmUKUlNT4eHhgWnTpqFp06bIy8vDiRMnsHLlSuTn56Nly5Yqu1y0b98eSUlJ4hKzHTp0EFs3zM3Ny73k65w5c7B3717cuXMHQUFBSEhIgK+vL+zs7MRlqWVLY3t4eMhNfVaZsrOz0bdvXzg6OmLAgAFo27Yt6tatCz09PTx48AAHDx4UF0apVauW3HRawMvrJRMQEIBZs2bBzs5O/OfP0dERenp6GDx4MGbOnIn8/Hz4+voiPj4eH374ISwsLJCYmIiffvoJMTEx6NChg8qvUGXnk0qlGDt2LCZMmCC3sEODBg3En8eMGYO1a9fixYsX6NOnD+bMmYOOHTuioKAA58+fx/Lly1FYWAhnZ2elX5cmJyeja9eucHFxwccffwx3d3dxIFZKSgp2794tJktubm5lTvdXlf3www84duwYnj59ik8//RSnT5/GoEGDYG5uLi5LffPmTXHMQFWwbt06XLp0CXfu3MGkSZPw119/YcSIEWjSpAkMDQ3x5MkTXL58GeHh4Thx4gQ+/vhjpQullNfOnTvRpk0bZGRkYMiQIdi+fTuGDBkCZ2dn6Orqiku4Hzx4EH///TcmTZokzoyiSXp6emjdujXOnz+PLVu2oEWLFnBzcxOnE7Wysqr08Q0GBgbYsGED+vTpg+zsbHTo0AFTpkyBp6cndHV1ERkZicWLF4uzhCxbtkzpgizt27fHyZMncfHiRSxevBg9e/YUl6M2NjYWP382NjbYtm0bBg0ahAcPHsDd3R0jR45Ez549Ubt2bRQWFuL+/fuIjo7Gnj17cOfOHRw8eLBc05ECL5NkX19ftG7dGn369EHLli1ha2uLwsJCJCUlYevWreK3v3379q2y3wJWqspe4o/eTOVZlrq46OhosXz37t3l9hUUFAjjxo0TJBKJ3HKwyh5OTk5yxxZfalTVo1+/fsKzZ8+UxhYVFSUuWVzWY9u2bSWOV+c6FJednS0YGxvL1b9ixQqVxxQUFAjdu3cvNT5jY2Ph999/L3NZ47i4OMHQ0FBpHcWXSi3P0rBJSUlCo0aNVF63Dh06lLqkcXmXYN66datYX1JSksqyymIsz+trZ2cnXLp0SWkdgwcPLvW44vFs2bJFXLJX2WPIkCHC8ePHxd+VLY9bVFQktGvXrtQ6FC1fvrzUslZWVsKZM2dKXSq5+GdZ1aNRo0alLotclvIuS13W8sPlXc5XlZMnTwpmZmalPs85c+aUuQx5ZS5LLQiC8ODBA+GDDz4o1+vk6+v7yvHI3LhxQ2jatGm5zjdv3rwSx5f3dSrrsx8aGlrq3wV177flXVq++OdB2WdTEAQhJCSk1PsnAEFXV1dYtGhRqee4f/++YGVlpfRYZa/RgQMHSi1f/KGjoyOcOHGixPGlXbPi91RVj/bt2wuPHz9Wed3eFexuQRWidevW+PDDDwG8/Oq2+MAYfX19rF27FpcvX8aECRPQrFkzWFhYQFdXFxYWFnBzc4Ofnx/27NmDa9euydU7efJk/Pnnnxg3bhzatWuHOnXqwMjICEZGRnB0dMTgwYMRGhqK/fv3lzrQrV27dvj333+xfv169OrVC/b29jAwMICRkREcHBzQvXt3LFy4ENevX8eIESM0dk1kA6NkdHV1MXToUJXH6OvrIywsDKtWrYK7uztMTExgbGyMBg0aYOzYsYiNjVW5opaMm5sboqKiMGzYMNSpU0flak5lcXR0xOXLl7F69Wp07twZ1atXh76+PmxsbNCjRw/8+uuvOHPmjFZntahbty6io6MRFBSE7t27o2HDhrC0tISenh5q1KiBTp06YenSpbh+/bq44pai7du3Y8mSJeK3HMXnUy3O19cXZ8+eRf/+/WFtbQ19fX3Y2dmhR48e2L17N3bt2lXmLAU6Ojo4evQoZs+eDVdXV1SrVk3lYJ+AgACEh4fD29sb7733HgwNDeHk5AR/f3/ExcWpXIr7gw8+wKlTpzBjxgx07doVDRo0gJmZmfgadu/eHevXr0d8fPwb29WiuC5duiAxMRHjxo1D3bp1YWBgABsbG/Tq1Qvh4eGYN2+etkMswdbWFmfOnEFoaCiGDx+OevXqwcTEBPr6+rC2tkb79u0xadIknD59Glu2bHnt873//vuIj4/Hzp07MXDgQNSpUwfGxsYwMDCAnZ0dunTpgtmzZyMmJgZz5szRwDNUrlevXoiIiEC/fv1gb2+vcrn3yuTj44Pr16/jm2++QePGjWFqagpjY2PUr18fo0ePRlxcnDhrkTK1atVCdHQ0/Pz80KBBA5ULvAAv+6YnJSVh2bJl6NatG2xsbKCvrw9jY2M4OTmhd+/eWL58Oe7evYuuXbuW+3kMGzYMhw4dQkBAADp27AgnJyeYmJjAwMAAtWvXRt++fbFjxw6cPXtW5UJP7xKJIFSBSTuJiIiIiKoQtiQTERERESlgkkxEREREpIBJMhERERGRAibJREREREQKmCQTERERESlgkkxEREREpIAr7mmIVCpFamoqzMzMVM5vSkRERETaIQgCnj59Cnt7+1Lnv5dhkqwhqampcHBw0HYYRERERFSGlJQU1K5dW2UZJskaYmZmBuDlRTc3N9dyNERERESkKDs7Gw4ODmLepgqTZA2RdbEwNzdnkkxERERUhZWnaywH7hERERERKWCSTERERESkgEkyEREREZEC9kkmIiKiN0ZRUREKCwu1HQZVUbq6utDT09PIdLxMkomIiOiNkJOTg/v370MQBG2HQlWYiYkJ7OzsYGBg8Fr1MEkmIiKiKq+oqAj379+HiYkJrK2tuXAXlSAIAgoKCvDo0SMkJSXB2dm5zAVDVGGSTERERFVeYWEhBEGAtbU1jI2NtR0OVVHGxsbQ19fHvXv3UFBQACMjo1euiwP3iIiI6I3BFmQqy+u0HsvVo5FaiIiIiIjeIkySiYiIiIgUMEkmIiIiokoRFBQENze316rj7t27kEgkiI+P10hMpeHAPSIiqpJC45K0HQJVot4tnF7puMp+n7xqnK8jKCgI+/fvr/CkkOSxJZmIiIiISAGTZCIiIqIK0qVLF3z99deYOnUqrKysYGtri6CgILkyycnJ6NevH6pVqwZzc3MMHjwYDx8+BACEhIRg3rx5uHz5MiQSCSQSCUJCQko935YtW9CkSRMYGhrCzs4O48ePF/ctX74czZo1g6mpKRwcHPDVV18hJydH3B8SEgJLS0scOXIEjRs3RrVq1dCjRw88ePCg3OfIzMzEqFGjYG1tDXNzc3Tr1g2XL19WeY02bdqExo0bw8jICI0aNcLatWvl9kdHR6NFixYwMjKCu7s74uLiVNanKUySiYiIiCrQtm3bYGpqigsXLmDJkiWYP38+jh07BgCQSqXo168fMjIycPr0aRw7dgx37tzBkCFDAABDhgzBpEmT0KRJEzx48AAPHjwQ9ylat24d/P39MWbMGCQkJODAgQNo0KCBuF9HRwerVq1CYmIitm3bhhMnTmDq1KlydTx79gzLli3Dr7/+ijNnziA5ORmTJ08u9zk++eQTpKen4/Dhw4iJiUHLli3h6emJjIwMpTHv2LEDc+bMwcKFC3Ht2jUsWrQI3377LbZt2wbg5SqLvXv3houLC2JiYhAUFCQXT0Vin2QiIiKiCtS8eXPMnTsXAODs7IzVq1cjIiICH374ISIiIpCQkICkpCQ4ODgAAH755Rc0adIEFy9eROvWrVGtWjXo6enB1tZW5XkWLFiASZMm4ZtvvhG3tW7dWvx54sSJ4s+Ojo5YsGABxo4dK9dyW1hYiPXr16N+/foAgPHjx2P+/PnlOse5c+cQHR2N9PR0GBoaAgCWLVuG/fv3Y8+ePRgzZkyJmOfOnYsffvgBAwYMAAA4OTnh6tWr+Pnnn+Hj44OdO3dCKpVi8+bNMDIyQpMmTXD//n2MGzdO5bXQBCbJRERERBWoefPmcr/b2dkhPT0dAHDt2jU4ODiICTIAuLi4wNLSEteuXZNLclVJT09HamoqPD09Sy1z/PhxBAcH4/r168jOzsaLFy+Ql5eHZ8+ewcTEBABgYmIiJsiKsZZ1jsuXLyMnJwfVq1eX2/78+XPcvn27RPnc3Fzcvn0bfn5+GD16tLj9xYsXsLCwAPDy+jRv3lxu5TwPD4+yLodGMEkmIiIiqkD6+vpyv0skEkilUo2eo6yluu/evYvevXtj3LhxWLhwIaysrHDu3Dn4+fmhoKBATJKVxSoIQrnOkZOTAzs7O5w6darEPktLS6XlAWDjxo1o27at3D5dXV2V56oMTJKJiIiItKRx48ZISUlBSkqK2Jp89epVZGZmwsXFBQBgYGCAoqIilfWYmZnB0dERERER6Nq1a4n9MTExkEql+OGHH8Rlm3///Xe1Yi3rHC1btkRaWhr09PTg6OhYZn02Njawt7fHnTt3MHz4cKVlGjdujF9//RV5eXlia/Lff/+tVtyvigP3iIiIiLTEy8sLzZo1w/DhwxEbG4vo6GiMGDECnTt3hru7O4CX/YeTkpIQHx+Px48fIz8/X2ldQUFB+OGHH7Bq1Sr8+++/iI2NxU8//QQAaNCgAQoLC/HTTz/hzp07+PXXX7F+/Xq141V1Di8vL3h4eKB///44evQo7t69i8jISMyaNQuXLl1SWt+8efMQHByMVatW4ebNm0hISMDWrVuxfPlyAMCnn34KiUSC0aNH4+rVqzh06BCWLVumdtyvgi3JRERE9MbSxuIemiSRSPDXX39hwoQJ6NSpE3R0dNCjRw8x8QSAgQMHYu/evejatSsyMzOxdetWjBw5skRdPj4+yMvLw4oVKzB58mTUqFEDgwYNAgC4urpi+fLl+P777zFjxgx06tQJwcHBGDFihFrxqjqHRCLBoUOHMGvWLPj6+uLRo0ewtbVFp06dYGNjo7S+UaNGwcTEBEuXLsWUKVNgamqKZs2aiYMMq1WrhoMHD2Ls2LFo0aIFXFxc8P3332PgwIFqxf0qJIKsowm9luzsbFhYWCArKwvm5ubaDoeI6I3HFffeLWUlu3l5eUhKSoKTk5PcIC4iRareK+rka+xuQURERESkgEkyEREREZECJslERERERAqYJBMRERERKWCSTERERESkgEkyEREREZECJslERERERAqYJBMRERERKWCSTERERESkgMtSExER0Rur58L9lXq+w7P6V+r5KsPIkSORmZmJ/fv3v3Idp06dQteuXfHff//B0tJSY7FpE1uSiYiIiIgUMEkmIiIiIlLAJJmIiIioAkmlUixZsgQNGjSAoaEh6tSpg4ULFwIApk2bhvfffx8mJiaoV68evv32WxQWForHBgUFwc3NDb/++iscHR1hYWGBoUOH4unTp+WqHwBSUlIwePBgWFpawsrKCv369cPdu3dVxhscHAwnJycYGxvD1dUVe/bskStz6NAhvP/++zA2NkbXrl1V1vemYpJMREREVIFmzJiBxYsX49tvv8XVq1exc+dO2NjYAADMzMwQEhKCq1ev4scff8TGjRuxYsUKueNv376N/fv3IzQ0FKGhoTh9+jQWL15crvoLCwvh7e0NMzMznD17FufPn0e1atXQo0cPFBQUKI03ODgYv/zyC9avX4/ExEQEBATgs88+w+nTpwG8TLoHDBiAPn36ID4+HqNGjcL06dMr4tJpFQfuEREREVWQp0+f4scff8Tq1avh4+MDAKhfvz46duwIAJg9e7ZY1tHREZMnT8auXbswdepUcbtUKkVISAjMzMwAAJ9//jkiIiKwcOHCMuvfvXs3pFIpNm3aBIlEAgDYunUrLC0tcerUKXTv3l0u3vz8fCxatAjHjx+Hh4cHAKBevXo4d+4cfv75Z3Tu3Bnr1q1D/fr18cMPPwAAGjZsiISEBHz//fcav37axCSZiIiIqIJcu3YN+fn58PT0VLp/9+7dWLVqFW7fvo2cnBy8ePEC5ubmcmUcHR3FBBkA7OzskJ6eXq76L1++jFu3bskdDwB5eXm4fft2ifK3bt3Cs2fP8OGHH8ptLygoQIsWLcRztm3bVm6/LKF+mzBJJiIiIqogxsbGpe6LiorC8OHDMW/ePHh7e8PCwgK7du0SW2hl9PX15X6XSCSQSqVl1g8AOTk5aNWqFXbs2FFin7W1tdLyABAWFoZatWrJ7TM0NFR5rrcNk2QiIiKiCuLs7AxjY2NERERg1KhRcvsiIyNRt25dzJo1S9x27949jdUPAC1btsTu3btRs2bNEi3Uyri4uMDQ0BDJycno3Lmz0jKNGzfGgQMH5Lb9/fffasX9JmCSTERERFRBjIyMMG3aNEydOhUGBgbo0KEDHj16hMTERDg7OyM5ORm7du1C69atERYWhn379mmsfj8/PwwfPhxLly5Fv379MH/+fNSuXRv37t3D3r17MXXqVNSuXVuuPjMzM0yePBkBAQGQSqXo2LEjsrKycP78eZibm8PHxwdjx47FDz/8gClTpmDUqFGIiYlBSEiIBq9a1cAkmYiIiN5Yb8IKeN9++y309PQwZ84cpKamws7ODmPHjoWfnx8CAgIwfvx45Ofno1evXvj2228RFBSkkfoBwMTEBGfOnMG0adMwYMAAPH36FLVq1YKnp2epLcvfffcdrK2tERwcjDt37sDS0hItW7bEzJkzAQB16tTBn3/+iYCAAPz0009o06YNFi1ahC+++OK1rlNVIxEEQdB2EG+D7OxsWFhYICsrq1xfZxARkWqhcUnaDoEqUe8WTir35+XlISkpCU5OTjAyMqqkqOhNpOq9ok6+xnmSiYiIiIgUMEkmIiIiIlLAJJmIiIiISAGTZCIiIiIiBVUmSV68eDEkEgkmTpwobsvLy4O/vz+qV6+OatWqYeDAgXj48KHcccnJyejVqxdMTExQs2ZNTJkyBS9evJArc+rUKbRs2RKGhoZo0KCB0mlK1qxZA0dHRxgZGaFt27aIjo6uiKdJREREr4HzDVBZNPUeqRJJ8sWLF/Hzzz+jefPmctsDAgJw8OBB/PHHHzh9+jRSU1MxYMAAcX9RURF69eqFgoICREZGYtu2bQgJCcGcOXPEMklJSejVqxe6du2K+Ph4TJw4EaNGjcKRI0fEMrt370ZgYCDmzp2L2NhYuLq6wtvbW1zykYiIiLRLV1cXwMvlkYlUefbsGYCSKxWqS+tTwOXk5KBly5ZYu3YtFixYADc3N6xcuRJZWVmwtrbGzp07MWjQIADA9evX0bhxY0RFRaFdu3Y4fPgwevfujdTUVNjY2AAA1q9fj2nTpuHRo0cwMDDAtGnTEBYWhitXrojnHDp0KDIzMxEeHg4AaNu2LVq3bo3Vq1cDAKRSKRwcHDBhwgRMnz69XM+DU8AREWkWp4B7t5Q1BZwgCEhOTkZhYSHs7e2ho1Ml2vmoChEEAc+ePUN6ejosLS1hZ2dXoow6+ZrWFxPx9/dHr1694OXlhQULFojbY2JiUFhYCC8vL3Fbo0aNUKdOHTFJjoqKQrNmzcQEGQC8vb0xbtw4JCYmokWLFoiKipKrQ1ZG1q2joKAAMTExmDFjhrhfR0cHXl5eiIqKKjXu/Px85Ofni79nZ2e/8jUgIiIi1SQSCezs7JCUlKT20s30brG0tIStre1r16PVJHnXrl2IjY3FxYsXS+xLS0uDgYEBLC0t5bbb2NggLS1NLFM8QZbtl+1TVSY7OxvPnz/Hf//9h6KiIqVlrl+/XmrswcHBmDdvXvmeKBEREb02AwMDODs7s8sFlUpfX1/smvO6tJYkp6Sk4JtvvsGxY8feyJVzZsyYgcDAQPH37OxsODg4aDEiIiKit5+Ojs4bmTfQm0drHXpiYmKQnp6Oli1bQk9PD3p6ejh9+jRWrVoFPT092NjYoKCgAJmZmXLHPXz4UGxCt7W1LTHbhez3ssqYm5vD2NgYNWrUgK6urtIyqprqDQ0NYW5uLvcgIiIioreD1pJkT09PJCQkID4+Xny4u7tj+PDh4s/6+vqIiIgQj7lx4waSk5Ph4eEBAPDw8EBCQoLcLBTHjh2Dubk5XFxcxDLF65CVkdVhYGCAVq1ayZWRSqWIiIgQyxARERHRu0Vr3S3MzMzQtGlTuW2mpqaoXr26uN3Pzw+BgYGwsrKCubk5JkyYAA8PD7Rr1w4A0L17d7i4uODzzz/HkiVLkJaWhtmzZ8Pf3x+GhoYAgLFjx2L16tWYOnUqvvjiC5w4cQK///47wsLCxPMGBgbCx8cH7u7uaNOmDVauXInc3Fz4+vpW0tUgIiIioqpE67NbqLJixQro6Ohg4MCByM/Ph7e3N9auXSvu19XVRWhoKMaNGwcPDw+YmprCx8cH8+fPF8s4OTkhLCwMAQEB+PHHH1G7dm1s2rQJ3t7eYpkhQ4bg0aNHmDNnDtLS0uDm5obw8PASg/mIiIiI6N2g9XmS3xacJ5mISLM4T/K7pax5kok0QZ18jTNxExEREREpYJJMRERERKSASTIRERERkQImyURERERECpgkExEREREpYJJMRERERKSASTIRERERkQImyURERERECpgkExEREREpYJJMRERERKSASTIRERERkQImyURERERECpgkExEREREpYJJMRERERKRA7SQ5NjYWCQkJ4u9//fUX+vfvj5kzZ6KgoECjwRERERERaYPaSfKXX36JmzdvAgDu3LmDoUOHwsTEBH/88QemTp2q8QCJiIiIiCqb2knyzZs34ebmBgD4448/0KlTJ+zcuRMhISH4888/NR0fEREREVGlUztJFgQBUqkUAHD8+HF89NFHAAAHBwc8fvxYs9EREREREWmB2kmyu7s7FixYgF9//RWnT59Gr169AABJSUmwsbHReIBERERERJVN7SR55cqViI2Nxfjx4zFr1iw0aNAAALBnzx60b99e4wESEREREVU2PXUPaN68udzsFjJLly6Frq6uRoIiIiIiItImtZNkmYKCAqSnp4v9k2Xq1Knz2kEREREREWmT2knyzZs34efnh8jISLntgiBAIpGgqKhIY8EREREREWmD2kmyr68v9PT0EBoaCjs7O0gkkoqIi4iIiIhIa9ROkuPj4xETE4NGjRpVRDxERERERFqn9uwWLi4unA+ZiIiIiN5qaifJ33//PaZOnYpTp07hyZMnyM7OlnsQEREREb3p1O5u4eXlBQDw9PSU286Be0RERET0tlA7ST558mRFxEFEREREVGWonSR37ty5IuIgIiIiIqoyXmkxkczMTGzevBnXrl0DADRp0gRffPEFLCwsNBocEREREZE2qD1w79KlS6hfvz5WrFiBjIwMZGRkYPny5ahfvz5iY2MrIkYiIiIiokqldktyQEAA+vbti40bN0JP7+XhL168wKhRozBx4kScOXNG40ESEREREVUmtZPkS5cuySXIAKCnp4epU6fC3d1do8EREREREWmD2t0tzM3NkZycXGJ7SkoKzMzMNBIUEREREZE2qZ0kDxkyBH5+fti9ezdSUlKQkpKCXbt2YdSoURg2bFhFxEhEREREVKnU7m6xbNkySCQSjBgxAi9evAAA6OvrY9y4cVi8eLHGAyQiIiIiqmwSQRCEVznw2bNnuH37NgCgfv36MDEx0Whgb5rs7GxYWFggKysL5ubm2g6HiOiNFxqXpO0QqBKtOXRZ2yFQJTo8q79WzqtOvvZK8yQDgImJCZo1a/aqhxMRERERVVnlSpIHDBiAkJAQmJubY8CAASrL7t27VyOBERERERFpS7mSZAsLC0gkEgAvZ7eQ/UxERERE9DYqV5K8detW8eeQkJCKioWIiIiIqEpQewq4bt26ITMzs8T27OxsdOvWTRMxERERERFpldpJ8qlTp1BQUFBie15eHs6ePauRoIiIiIiItKncs1v8888/4s9Xr15FWlqa+HtRURHCw8NRq1YtzUZHRERERKQF5U6S3dzcIJFIIJFIlHarMDY2xk8//aTR4IiIiIiItKHcSXJSUhIEQUC9evUQHR0Na2trcZ+BgQFq1qwJXV3dCgmSiIiIiKgylTtJrlu3LgBAKpVWWDBERERERFXBK6+4d/XqVSQnJ5cYxNe3b9/XDoqIiIiISJvUTpLv3LmDjz/+GAkJCZBIJBAEAQDEBUaKioo0GyERERERUSVTewq4b775Bk5OTkhPT4eJiQkSExNx5swZuLu749SpUxUQIhERERFR5VK7JTkqKgonTpxAjRo1oKOjAx0dHXTs2BHBwcH4+uuvERcXVxFxEhERERFVGrVbkouKimBmZgYAqFGjBlJTUwG8HNh348YNtepat24dmjdvDnNzc5ibm8PDwwOHDx8W9+fl5cHf3x/Vq1dHtWrVMHDgQDx8+FCujuTkZPTq1QsmJiaoWbMmpkyZghcvXsiVOXXqFFq2bAlDQ0M0aNBA6dLaa9asgaOjI4yMjNC2bVtER0er9VyIiIiI6O2hdpLctGlTXL58GQDQtm1bLFmyBOfPn8f8+fNRr149teqqXbs2Fi9ejJiYGFy6dAndunVDv379kJiYCAAICAjAwYMH8ccff+D06dNITU3FgAEDxOOLiorQq1cvFBQUIDIyEtu2bUNISAjmzJkjlklKSkKvXr3QtWtXxMfHY+LEiRg1ahSOHDkiltm9ezcCAwMxd+5cxMbGwtXVFd7e3khPT1f38hARERHRW0AiyEbeldORI0eQm5uLAQMG4NatW+jduzdu3ryJ6tWrY/fu3UoXGlGHlZUVli5dikGDBsHa2ho7d+7EoEGDAADXr19H48aNERUVhXbt2uHw4cPo3bs3UlNTYWNjAwBYv349pk2bhkePHsHAwADTpk1DWFgYrly5Ip5j6NChyMzMRHh4OICXyX7r1q2xevVqAC+nuXNwcMCECRMwffr0csWdnZ0NCwsLZGVlwdzc/LWuARERAaFxSdoOgSrRmkOXtR0CVaLDs/pr5bzq5GtqtyR7e3uLrbkNGjTA9evX8fjxY6Snp79WglxUVIRdu3YhNzcXHh4eiImJQWFhIby8vMQyjRo1Qp06dRAVFQXgZf/oZs2aiQmyLL7s7GyxNToqKkquDlkZWR0FBQWIiYmRK6OjowMvLy+xjDL5+fnIzs6WexARERHR20HtJHn79u3Izc2V22ZlZSVOAaeuhIQEVKtWDYaGhhg7diz27dsHFxcXpKWlwcDAAJaWlnLlbWxskJaWBgBIS0uTS5Bl+2X7VJXJzs7G8+fP8fjxYxQVFSktI6tDmeDgYFhYWIgPBweHV3r+RERERFT1qJ0kBwQEwMbGBp9++ikOHTr02vMiN2zYEPHx8bhw4QLGjRsHHx8fXL169bXqrAwzZsxAVlaW+EhJSdF2SERERESkIWonyQ8ePMCuXbsgkUgwePBg2NnZwd/fH5GRka8UgIGBARo0aIBWrVohODgYrq6u+PHHH2Fra4uCggJkZmbKlX/48CFsbW0BALa2tiVmu5D9XlYZc3NzGBsbo0aNGtDV1VVaRlaHMoaGhuKsHLIHEREREb0d1E6S9fT00Lt3b+zYsQPp6elYsWIF7t69i65du6J+/fqvHZBUKkV+fj5atWoFfX19REREiPtu3LiB5ORkeHh4AAA8PDyQkJAgNwvFsWPHYG5uDhcXF7FM8TpkZWR1GBgYoFWrVnJlpFIpIiIixDJERERE9G5RezGR4kxMTODt7Y3//vsP9+7dw7Vr19Q6fsaMGejZsyfq1KmDp0+fYufOnTh16hSOHDkCCwsL+Pn5ITAwEFZWVjA3N8eECRPg4eGBdu3aAQC6d+8OFxcXfP7551iyZAnS0tIwe/Zs+Pv7w9DQEAAwduxYrF69GlOnTsUXX3yBEydO4Pfff0dYWJgYR2BgIHx8fODu7o42bdpg5cqVyM3Nha+v7+tcHiIiIiJ6Q71Skvzs2TPs27cPO3bsQEREBBwcHDBs2DDs2bNHrXrS09MxYsQIPHjwABYWFmjevDmOHDmCDz/8EACwYsUK6OjoYODAgcjPz4e3tzfWrl0rHq+rq4vQ0FCMGzcOHh4eMDU1hY+PD+bPny+WcXJyQlhYGAICAvDjjz+idu3a2LRpE7y9vcUyQ4YMwaNHjzBnzhykpaXBzc0N4eHhJQbzEREREdG7Qe15kocOHYrQ0FCYmJhg8ODBGD58OLslgPMkExFpGudJfrdwnuR3y5swT7LaLcm6urr4/fff4e3tDV1d3VcOkoiIiIioqlJr4F5hYSHS0tLg7OzMBJmIiIiI3lpqJcn6+vr4559/KioWIiIiIqIqQe3uFp999hk2b96MxYsXV0Q89IrYd+/d0ruFk7ZDICIiequpnSS/ePECW7ZswfHjx9GqVSuYmprK7V++fLnGgiMiIiIi0ga1k+QrV66gZcuWAICbN2/K7ZNIJJqJioiIiIhIi9ROkk+ePFkRcRARERERVRlqL0stc+vWLRw5cgTPnz8HAKg53TIRERERUZWldpL85MkTeHp64v3338dHH32EBw8eAAD8/PwwadIkjQdIRERERFTZ1E6SAwICoK+vj+TkZJiYmIjbhwwZgvDwcI0GR0RERESkDWr3ST569CiOHDmC2rVry213dnbGvXv3NBYYEREREZG2qN2SnJubK9eCLJORkQFDQ0ONBEVEREREpE1qJ8kffPABfvnlF/F3iUQCqVSKJUuWoGvXrhoNjoiIiIhIG9TubrFkyRJ4enri0qVLKCgowNSpU5GYmIiMjAycP3++ImIkIiIiIqpUarckN23aFDdv3kTHjh3Rr18/5ObmYsCAAYiLi0P9+vUrIkYiIiIiokqldksyAFhYWGDWrFmajoWIiIiIqEpQuyU5PDwc586dE39fs2YN3Nzc8Omnn+K///7TaHBERERERNqgdpI8ZcoUZGdnAwASEhIQGBiIjz76CElJSQgMDNR4gERERERElU3t7hZJSUlwcXEBAPz555/o06cPFi1ahNjYWHz00UcaD5CIiIiIqLKp3ZJsYGCAZ8+eAQCOHz+O7t27AwCsrKzEFmYiIiIiojeZ2i3JHTt2RGBgIDp06IDo6Gjs3r0bAHDz5s0Sq/AREREREb2J1G5JXr16NfT09LBnzx6sW7cOtWrVAgAcPnwYPXr00HiARERERESVTe2W5Dp16iA0NLTE9hUrVmgkICIiIiIibVO7JRkAbt++jdmzZ2PYsGFIT08H8LIlOTExUaPBERERERFpg9pJ8unTp9GsWTNcuHABe/fuRU5ODgDg8uXLmDt3rsYDJCIiIiKqbGonydOnT8eCBQtw7NgxGBgYiNu7deuGv//+W6PBERERERFpg9pJckJCAj7++OMS22vWrInHjx9rJCgiIiIiIm1SO0m2tLTEgwcPSmyPi4sTZ7ogIiIiInqTqZ0kDx06FNOmTUNaWhokEgmkUinOnz+PyZMnY8SIERURIxERERFRpVI7SV60aBEaNWoEBwcH5OTkwMXFBZ06dUL79u0xe/bsioiRiIiIiKhSqTVPsiAISEtLw6pVqzBnzhwkJCQgJycHLVq0gLOzc0XFSERERERUqdROkhs0aIDExEQ4OzvDwcGhouIiIiIiItIatbpb6OjowNnZGU+ePKmoeIiIiIiItE7tPsmLFy/GlClTcOXKlYqIh4iIiIhI69TqbgEAI0aMwLNnz+Dq6goDAwMYGxvL7c/IyNBYcERERERE2qB2krxy5coKCIOIiIiIqOpQO0n28fGpiDiIiIiIiKoMtfskExERERG97ZgkExEREREpYJJMRERERKSgXEnyP//8A6lUWtGxEBERERFVCeVKklu0aIHHjx8DAOrVq8fFRIiIiIjorVauJNnS0hJJSUkAgLt377JVmYiIiIjeauWaAm7gwIHo3Lkz7OzsIJFI4O7uDl1dXaVl79y5o9EAiYiIiIgqW7mS5A0bNmDAgAG4desWvv76a4wePRpmZmYVHRsRERERkVaUezGRHj16AABiYmLwzTffMEkmIiIioreW2ivubd26Vfz5/v37AIDatWtrLiIiIiIiIi1Te55kqVSK+fPnw8LCAnXr1kXdunVhaWmJ7777jgP6iIiIiOitoHZL8qxZs7B582YsXrwYHTp0AACcO3cOQUFByMvLw8KFCzUeJBERERFRZVI7Sd62bRs2bdqEvn37ituaN2+OWrVq4auvvmKSTERERERvPLW7W2RkZKBRo0Yltjdq1AgZGRkaCYqIiIiISJvUTpJdXV2xevXqEttXr14NV1dXjQRFRERERKRNane3WLJkCXr16oXjx4/Dw8MDABAVFYWUlBQcOnRI4wESEREREVU2tVuSO3fujJs3b+Ljjz9GZmYmMjMzMWDAANy4cQMffPCBWnUFBwejdevWMDMzQ82aNdG/f3/cuHFDrkxeXh78/f1RvXp1VKtWDQMHDsTDhw/lyiQnJ6NXr14wMTFBzZo1MWXKFLx48UKuzKlTp9CyZUsYGhqiQYMGCAkJKRHPmjVr4OjoCCMjI7Rt2xbR0dFqPR8iIiIiejuo3ZIMAPb29hoZoHf69Gn4+/ujdevWePHiBWbOnInu3bvj6tWrMDU1BQAEBAQgLCwMf/zxBywsLDB+/HgMGDAA58+fBwAUFRWhV69esLW1RWRkJB48eIARI0ZAX18fixYtAgAkJSWhV69eGDt2LHbs2IGIiAiMGjUKdnZ28Pb2BgDs3r0bgYGBWL9+Pdq2bYuVK1fC29sbN27cQM2aNV/7uRIRERHRm0MiCIKg7SBkHj16hJo1a+L06dPo1KkTsrKyYG1tjZ07d2LQoEEAgOvXr6Nx48aIiopCu3btcPjwYfTu3RupqamwsbEBAKxfvx7Tpk3Do0ePYGBggGnTpiEsLAxXrlwRzzV06FBkZmYiPDwcANC2bVu0bt1a7G8tlUrh4OCACRMmYPr06WXGnp2dDQsLC2RlZcHc3FzTl6ZMoXFJlX5O0p7eLZy0HQJRheN97d2y5tBlbYdAlejwrP5aOa86+Zra3S0qUlZWFgDAysoKwMslsAsLC+Hl5SWWadSoEerUqYOoqCgAL/tDN2vWTEyQAcDb2xvZ2dlITEwUyxSvQ1ZGVkdBQQFiYmLkyujo6MDLy0ssoyg/Px/Z2dlyDyIiIiJ6O1SZJFkqlWLixIno0KEDmjZtCgBIS0uDgYEBLC0t5cra2NggLS1NLFM8QZbtl+1TVSY7OxvPnz/H48ePUVRUpLSMrA5FwcHBsLCwEB8ODg6v9sSJiIiIqMpRK0kWBAHJycnIy8vTeCD+/v64cuUKdu3apfG6K8KMGTOQlZUlPlJSUrQdEhERERFpiNpJcoMGDTSeEI4fPx6hoaE4efIkateuLW63tbVFQUEBMjMz5co/fPgQtra2YhnF2S5kv5dVxtzcHMbGxqhRowZ0dXWVlpHVocjQ0BDm5uZyDyIiIiJ6O6iVJOvo6MDZ2RlPnjzRyMkFQcD48eOxb98+nDhxAk5O8oORWrVqBX19fURERIjbbty4geTkZHGOZg8PDyQkJCA9PV0sc+zYMZibm8PFxUUsU7wOWRlZHQYGBmjVqpVcGalUioiICLEMEREREb071O6TvHjxYkyZMkVupohX5e/vj+3bt2Pnzp0wMzNDWloa0tLS8Pz5cwCAhYUF/Pz8EBgYiJMnTyImJga+vr7w8PBAu3btAADdu3eHi4sLPv/8c1y+fBlHjhzB7Nmz4e/vD0NDQwDA2LFjcefOHUydOhXXr1/H2rVr8fvvvyMgIECMJTAwEBs3bsS2bdtw7do1jBs3Drm5ufD19X3t50lEREREbxa150keMWIEnj17BldXVxgYGMDY2Fhuf0ZGRrnrWrduHQCgS5cuctu3bt2KkSNHAgBWrFgBHR0dDBw4EPn5+fD29sbatWvFsrq6uggNDcW4cePg4eEBU1NT+Pj4YP78+WIZJycnhIWFISAgAD/++CNq166NTZs2iXMkA8CQIUPw6NEjzJkzB2lpaXBzc0N4eHiJwXxERERE9PZTe57kbdu2qdzv4+PzWgG9qThPMlUmzpNM7wLe194tnCf53fImzJOsdkvyu5oEExEREdG745XmSb59+zZmz56NYcOGiQPmDh8+LC7eQURERET0JlM7ST59+jSaNWuGCxcuYO/evcjJyQEAXL58GXPnztV4gERERERElU3tJHn69OlYsGABjh07BgMDA3F7t27d8Pfff2s0OCIiIiIibVA7SU5ISMDHH39cYnvNmjXx+PFjjQRFRERERKRNaifJlpaWePDgQYntcXFxqFWrlkaCIiIiIiLSJrWT5KFDh2LatGlIS0uDRCKBVCrF+fPnMXnyZIwYMaIiYiQiIiIiqlRqJ8mLFi1Co0aN4ODggJycHLi4uKBTp05o3749Zs+eXRExEhERERFVKrXnSTYwMMDGjRvx7bff4sqVK8jJyUGLFi3g7OxcEfEREREREVU6tZNkmTp16sDBwQEAIJFINBYQEREREZG2vdJiIps3b0bTpk1hZGQEIyMjNG3aFJs2bdJ0bEREREREWqF2S/KcOXOwfPlyTJgwAR4eHgCAqKgoBAQEIDk5GfPnz9d4kERERERElUntJHndunXYuHEjhg0bJm7r27cvmjdvjgkTJjBJJiIiIqI3ntrdLQoLC+Hu7l5ie6tWrfDixQuNBEVEREREpE1qJ8mff/451q1bV2L7hg0bMHz4cI0ERURERESkTeXqbhEYGCj+LJFIsGnTJhw9ehTt2rUDAFy4cAHJyclcTISIiIiI3grlSpLj4uLkfm/VqhUA4Pbt2wCAGjVqoEaNGkhMTNRweEREREREla9cSfLJkycrOg4iIiIioirjleZJJiIiIiJ6m6k9BVxeXh5++uknnDx5Eunp6ZBKpXL7Y2NjNRYcEREREZE2qJ0k+/n54ejRoxg0aBDatGnDJamJiIiI6K2jdpIcGhqKQ4cOoUOHDhURDxERERGR1qndJ7lWrVowMzOriFiIiIiIiKoEtZPkH374AdOmTcO9e/cqIh4iIiIiIq1Tu7uFu7s78vLyUK9ePZiYmEBfX19uf0ZGhsaCIyIiIiLSBrWT5GHDhuF///sfFi1aBBsbGw7cIyIiIqK3jtpJcmRkJKKiouDq6loR8RARERERaZ3afZIbNWqE58+fV0QsRERERERVgtpJ8uLFizFp0iScOnUKT548QXZ2ttyDiIiIiOhNp3Z3ix49egAAPD095bYLggCJRIKioiLNREZEREREpCVqJ8knT56siDiIiIiIiKoMtZPkzp07V0QcRERERERVhtpJ8pkzZ1Tu79Sp0ysHQ0RERERUFaidJHfp0qXEtuJzJbNPMhERERG96dSe3eK///6Te6SnpyM8PBytW7fG0aNHKyJGIiIiIqJKpXZLsoWFRYltH374IQwMDBAYGIiYmBiNBEZEREREpC1qtySXxsbGBjdu3NBUdUREREREWqN2S/I///wj97sgCHjw4AEWL14MNzc3TcVFRERERKQ1aifJbm5ukEgkEARBbnu7du2wZcsWjQVGRERERKQtaifJSUlJcr/r6OjA2toaRkZGGguKiIiIiEib1E6S69atWxFxEBERERFVGWonyQAQERGBiIgIpKenQyqVyu1jlwsiIiIietOpnSTPmzcP8+fPh7u7O+zs7OQWEiEiIiIiehuonSSvX78eISEh+PzzzysiHiIiIiIirVN7nuSCggK0b9++ImIhIiIiIqoS1E6SR40ahZ07d1ZELEREREREVYLa3S3y8vKwYcMGHD9+HM2bN4e+vr7c/uXLl2ssOCIiIiIibXilFfdkK+tduXJFbh8H8RERERHR20DtJPnkyZMVEQcRERERUZXxSvMkE5F29Vy4X9shUCU6PKu/tkMgInrnqD1wj4iIiIjobcckmYiIiIhIgVaT5DNnzqBPnz6wt7eHRCLB/v375fYLgoA5c+bAzs4OxsbG8PLywr///itXJiMjA8OHD4e5uTksLS3h5+eHnJwcuTL//PMPPvjgAxgZGcHBwQFLliwpEcsff/yBRo0awcjICM2aNcOhQ4c0/nyJiIiI6M2g1SQ5NzcXrq6uWLNmjdL9S5YswapVq7B+/XpcuHABpqam8Pb2Rl5enlhm+PDhSExMxLFjxxAaGoozZ85gzJgx4v7s7Gx0794ddevWRUxMDJYuXYqgoCBs2LBBLBMZGYlhw4bBz88PcXFx6N+/P/r3719i9g4iIiIiejdIBEEQtB0E8HL6uH379qF///4AXrYi29vbY9KkSZg8eTIAICsrCzY2NggJCcHQoUNx7do1uLi44OLFi3B3dwcAhIeH46OPPsL9+/dhb2+PdevWYdasWUhLS4OBgQEAYPr06di/fz+uX78OABgyZAhyc3MRGhoqxtOuXTu4ublh/fr15Yo/OzsbFhYWyMrKgrm5uaYuS7mFxiVV+jlJe9YcuqztEKgSvasD93hfe7fwvvZu0dZ9TZ18rcr2SU5KSkJaWhq8vLzEbRYWFmjbti2ioqIAAFFRUbC0tBQTZADw8vKCjo4OLly4IJbp1KmTmCADgLe3N27cuIH//vtPLFP8PLIysvMok5+fj+zsbLkHEREREb0dqmySnJaWBgCwsbGR225jYyPuS0tLQ82aNeX26+npwcrKSq6MsjqKn6O0MrL9ygQHB8PCwkJ8ODg4qPsUiYiIiKiKqrJJclU3Y8YMZGVliY+UlBRth0REREREGlJlk2RbW1sAwMOHD+W2P3z4UNxna2uL9PR0uf0vXrxARkaGXBlldRQ/R2llZPuVMTQ0hLm5udyDiIiIiN4OVTZJdnJygq2tLSIiIsRt2dnZuHDhAjw8PAAAHh4eyMzMRExMjFjmxIkTkEqlaNu2rVjmzJkzKCwsFMscO3YMDRs2xHvvvSeWKX4eWRnZeYiIiIjo3aLVJDknJwfx8fGIj48H8HKwXnx8PJKTkyGRSDBx4kQsWLAABw4cQEJCAkaMGAF7e3txBozGjRujR48eGD16NKKjo3H+/HmMHz8eQ4cOhb29PQDg008/hYGBAfz8/JCYmIjdu3fjxx9/RGBgoBjHN998g/DwcPzwww+4fv06goKCcOnSJYwfP76yLwkRERERVQF62jz5pUuX0LVrV/F3WeLq4+ODkJAQTJ06Fbm5uRgzZgwyMzPRsWNHhIeHw8jISDxmx44dGD9+PDw9PaGjo4OBAwdi1apV4n4LCwscPXoU/v7+aNWqFWrUqIE5c+bIzaXcvn177Ny5E7Nnz8bMmTPh7OyM/fv3o2nTppVwFYiIiIioqqky8yS/6ThPMlUmzif6buE8yfQu4H3t3cJ5komIiIiI3kBMkomIiIiIFDBJJiIiIiJSwCSZiIiIiEgBk2QiIiIiIgVMkomIiIiIFDBJJiIiIiJSwCSZiIiIiEgBk2QiIiIiIgVMkomIiIiIFDBJJiIiIiJSwCSZiIiIiEgBk2QiIiIiIgVMkomIiIiIFDBJJiIiIiJSwCSZiIiIiEgBk2QiIiIiIgVMkomIiIiIFDBJJiIiIiJSwCSZiIiIiEgBk2QiIiIiIgVMkomIiIiIFDBJJiIiIiJSwCSZiIiIiEgBk2QiIiIiIgVMkomIiIiIFDBJJiIiIiJSwCSZiIiIiEgBk2QiIiIiIgVMkomIiIiIFDBJJiIiIiJSwCSZiIiIiEgBk2QiIiIiIgVMkomIiIiIFDBJJiIiIiJSwCSZiIiIiEgBk2QiIiIiIgVMkomIiIiIFDBJJiIiIiJSwCSZiIiIiEgBk2QiIiIiIgVMkomIiIiIFDBJJiIiIiJSwCSZiIiIiEgBk2QiIiIiIgVMkomIiIiIFDBJJiIiIiJSwCSZiIiIiEgBk2QiIiIiIgVMkomIiIiIFDBJJiIiIiJSwCSZiIiIiEgBk2QiIiIiIgVMkhWsWbMGjo6OMDIyQtu2bREdHa3tkIiIiIiokjFJLmb37t0IDAzE3LlzERsbC1dXV3h7eyM9PV3boRERERFRJWKSXMzy5csxevRo+Pr6wsXFBevXr4eJiQm2bNmi7dCIiIiIqBLpaTuAqqKgoAAxMTGYMWOGuE1HRwdeXl6IiooqUT4/Px/5+fni71lZWQCA7Ozsig9WiWc5T7VyXtKOF3nPtB0CVSJt3Ve0jfe1dwvva+8Wbd3XZOcVBKHMskyS/8/jx49RVFQEGxsbue02Nja4fv16ifLBwcGYN29eie0ODg4VFiMRvZssFmg7AiIizdL2fe3p06ewsLBQWYZJ8iuaMWMGAgMDxd+lUikyMjJQvXp1SCQSLUZGb7vs7Gw4ODggJSUF5ubm2g6HiOi18b5GlUUQBDx9+hT29vZllmWS/H9q1KgBXV1dPHz4UG77w4cPYWtrW6K8oaEhDA0N5bZZWlpWZIhEcszNzfnHhIjeKryvUWUoqwVZhgP3/o+BgQFatWqFiIgIcZtUKkVERAQ8PDy0GBkRERERVTa2JBcTGBgIHx8fuLu7o02bNli5ciVyc3Ph6+ur7dCIiIiIqBIxSS5myJAhePToEebMmYO0tDS4ubkhPDy8xGA+Im0yNDTE3LlzS3T3ISJ6U/G+RlWRRCjPHBhERERERO8Q9kkmIiIiIlLAJJmIiIiISAGTZCIiIiIiBUySiUij7t69C4lEgvj4eG2HQkQaJpFIsH//fm2HoXG8b5EyTJKJihk5ciQkEgkkEgn09fXh5OSEqVOnIi8vT9uhwdHREStXrnzlckFBQXBzcyv3+fhHg+jdkpaWhgkTJqBevXowNDSEg4MD+vTpI7d+wIMHD9CzZ08Amr1H8L5FVRGngCNS0KNHD2zduhWFhYWIiYmBj48PJBIJvv/+e63EU1BQAAMDA62cm4jeDXfv3kWHDh1gaWmJpUuXolmzZigsLMSRI0fg7++P69evA4DSFWiJ3lZsSSZSYGhoCFtbWzg4OKB///7w8vLCsWPHxP1SqRTBwcFwcnKCsbExXF1dsWfPHnH/f//9h+HDh8Pa2hrGxsZwdnbG1q1bxf0JCQno1q0bjI2NUb16dYwZMwY5OTni/pEjR6J///5YuHAh7O3t0bBhQ3Tp0gX37t1DQECA2NL9uqRSKebPn4/atWvD0NBQnBdcxsnJCQDQokULSCQSdOnSRdy3adMmNG7cGEZGRmjUqBHWrl372vEQkfZ89dVXkEgkiI6OxsCBA/H++++jSZMmCAwMxN9//y2WK97dQtk94syZM9DX10daWppc/RMnTsQHH3zw2nHyvkWViS3JRCpcuXIFkZGRqFu3rrgtODgY27dvx/r16+Hs7IwzZ87gs88+g7W1NTp37oxvv/0WV69exeHDh1GjRg3cunULz58/BwDk5ubC29sbHh4euHjxItLT0zFq1CiMHz8eISEh4jkiIiJgbm4uJud2dnZwdXXFmDFjMHr0aI08tx9//BE//PADfv75Z7Ro0QJbtmxB3759kZiYCGdnZ0RHR6NNmzY4fvw4mjRpIrZm79ixA3PmzMHq1avRokULxMXFYfTo0TA1NYWPj49GYiOiypORkYHw8HAsXLgQpqamJfZbWloqPU7ZPcLKygr16tXDr7/+iilTpgAACgsLsWPHDixZsuS1Y+V9iyqVQEQiHx8fQVdXVzA1NRUMDQ0FAIKOjo6wZ88eQRAEIS8vTzAxMREiIyPljvPz8xOGDRsmCIIg9OnTR/D19VVa/4YNG4T33ntPyMnJEbeFhYUJOjo6QlpamhiDjY2NkJ+fL3ds3bp1hRUrVpT5HOrWrSsYGBgIpqamcg99fX3B1dVVLGdvby8sXLhQ7tjWrVsLX331lSAIgpCUlCQAEOLi4uTK1K9fX9i5c6fctu+++07w8PBQeRwRVU0XLlwQAAh79+4tsywAYd++fYIglP5Z//7774XGjRuLv//5559CtWrV5O57injfoqqILclECrp27Yp169YhNzcXK1asgJ6eHgYOHAgAuHXrFp49e4YPP/xQ7piCggK0aNECADBu3DgMHDgQsbGx6N69O/r374/27dsDAK5duwZXV1e51poOHTpAKpXixo0b4hLozZo1e61+yFOmTMHIkSPltq1atQpnzpwBAGRnZyM1NRUdOnSQK9OhQwdcvny51Hpzc3Nx+/Zt+Pn5ybVov3jxAhYWFq8cLxFpj6DhhXdHjhyJ2bNn4++//0a7du0QEhKCwYMHK22lLo73LapqmCQTKTA1NUWDBg0AAFu2bIGrqys2b94MPz8/se9wWFgYatWqJXecoaEhAKBnz564d+8eDh06hGPHjsHT0xP+/v5YtmyZWjG8jho1aojPQcbKyuq16gQgPv+NGzeibdu2cvt0dXVfu34iqnzOzs6QSCTi4LzXVbNmTfTp0wdbt26Fk5MTDh8+jFOnTpV5HO9bVNVw4B6RCjo6Opg5cyZmz56N58+fw8XFBYaGhkhOTkaDBg3kHg4ODuJx1tbW8PHxwfbt27Fy5Ups2LABANC4cWNcvnwZubm5Ytnz589DR0cHDRs2VBmLgYEBioqKNPK8zM3NYW9vj/Pnz8ttP3/+PFxcXMTzAZA7p42NDezt7XHnzp0Sz182YIaI3ixWVlbw9vbGmjVr5O5NMpmZmUqPU3aPkBk1ahR2796NDRs2oH79+iVaf18F71tU2ZgkE5Xhk08+ga6uLtasWQMzMzNMnjwZAQEB2LZtG27fvo3Y2Fj89NNP2LZtGwBgzpw5+Ouvv3Dr1i0kJiYiNDQUjRs3BgAMHz4cRkZG8PHxwZUrV3Dy5ElMmDABn3/+udjVojSOjo44c+YM/ve//+Hx48ev/bymTJmC77//Hrt378aNGzcwffp0xMfH45tvvgHwsjXI2NgY4eHhePjwIbKysgAA8+bNQ3BwMFatWoWbN28iISEBW7duxfLly187JiLSjjVr1qCoqAht2rTBn3/+iX///RfXrl3DqlWr4OHhofSY0u4RAODt7Q1zc3MsWLAAvr6+GouT9y2qVNruFE1Ulfj4+Aj9+vUrsT04OFiwtrYWcnJyBKlUKqxcuVJo2LChoK+vL1hbWwve3t7C6dOnBUF4ORikcePGgrGxsWBlZSX069dPuHPnjljXP//8I3Tt2lUwMjISrKyshNGjRwtPnz4tM4aoqCihefPm4oDC0pQ2wG/u3LlyA2CKioqEoKAgoVatWuLgmMOHD8sds3HjRsHBwUHQ0dEROnfuLG7fsWOH4ObmJhgYGAjvvfee0KlTJ3HQDwfAEL2ZUlNTBX9/f3EQXa1atYS+ffsKJ0+eFMug2MA9QSj9HiEIgvDtt98Kurq6Qmpqapnn5n2LqiKJIGi4xz4RERG98/z8/PDo0SMcOHBA26EQvRIO3CMiIiKNycrKQkJCAnbu3MkEmd5oTJKJiIhIY/r164fo6GiMHTu2xHSZRG8SdrcgIiIiIlLA2S2IiIiIiBQwSSYiIiIiUsAkmYiIiIhIAZNkIiIiIiIFTJKJiIiIiBQwSSYieot16dIFEydOrPTzBgUFwc3NrdLPS0SkKUySiYioVEx2iehdxSSZiIiIiEgBk2QiorecVCrF1KlTYWVlBVtbWwQFBYn7kpOT0a9fP1SrVg3m5uYYPHgwHj58CAAICQnBvHnzcPnyZUgkEkgkEoSEhAAAMjMzMWrUKFhbW8Pc3BzdunXD5cuXtfDsiIgqBpNkIqK33LZt22BqaooLFy5gyZIlmD9/Po4dOwapVIp+/fohIyMDp0+fxrFjx3Dnzh0MGTIEADBkyBBMmjQJTZo0wYMHD/DgwQNx3yeffIL09HQcPnwYMTExaNmyJTw9PZGRkaHNp0pEpDF62g6AiIgqVvPmzTF37lwAgLOzM1avXo2IiAgAQEJCApKSkuDg4AAA+OWXX9CkSRNcvHgRrVu3RrVq1aCnpwdbW1uxvnPnziE6Ohrp6ekwNDQEACxbtgz79+/Hnj17MGbMmEp+hkREmsckmYjoLde8eXO53+3s7JCeno5r167BwcFBTJABwMXFBZaWlrh27Rpat26ttL7Lly8jJycH1atXl9v+/Plz3L59W/NPgIhIC5gkExG95fT19eV+l0gkkEqlr1xfTk4O7OzscOrUqRL7LC0tX7leIqKqhEkyEdE7qnHjxkhJSUFKSorYmnz16lVkZmbCxcUFAGBgYICioiK541q2bIm0tDTo6enB0dGxssMmIqoUHLhHRPSO8vLyQrNmzTB8+HDExsYiOjoaI0aMQOfOneHu7g4AcHR0RFJSEuLj4/H48WPk5+fDy8sLHh4e6N+/P44ePYq7d+8iMjISs2bNwqVLl7T8rIiININJMhHRO0oikeCvv/7Ce++9h06dOsHLywv16tXD7t27xTIDBw5Ejx490LVrV1hbW+O3336DRCLBoUOH0KlTJ/j6+uL999/H0KFDce/ePdjY2GjxGRERaY5EEARB20EQEREREVUlbEkmIiIiIlLAJJmIiIiISAGTZCIiIiIiBUySiYiIiIgUMEkmIiIiIlLAJJmIiIiISAGTZCIiIiIiBUySiYiIiIgUMEkmIiIiIlLAJJmIiIiISAGTZCIiIiIiBf8PPPcGx0cKWmcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,4))\n",
    "axl = sns.countplot(x = 'hotel', hue = 'is_canceled', data = df, palette = 'Blues')\n",
    "legend_labels,_=axl.get_legend_handles_labels()\n",
    "plt.title('Reservation status in different hotels', size = 20)\n",
    "plt.xlabel('hotel')\n",
    "plt.ylabel('number of reservations')\n",
    "plt.legend(['not canceled','canceled'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af9c912c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}