ml_hotel_booking_prediction_RF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "254f5edb",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59079f15",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "58369515",
   "metadata": {},
   "source": [
    "# # 1..read data.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c1cfafe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9427db25",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1cec96c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r'G:\\Project\\ML Hotel_booking\\hotel_bookings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6fe6f5f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b78ba637",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>342</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>7/1/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>737</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>7/1/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>7/2/2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 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",
       "\n",
       "   arrival_date_week_number  arrival_date_day_of_month  \\\n",
       "0                        27                          1   \n",
       "1                        27                          1   \n",
       "2                        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",
       "\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",
       "\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",
       "\n",
       "  reservation_status_date  \n",
       "0                7/1/2015  \n",
       "1                7/1/2015  \n",
       "2                7/2/2015  \n",
       "\n",
       "[3 rows x 32 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac7a1415",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3529fc9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "626f07f3",
   "metadata": {
    "raw_mimetype": "text/markdown"
   },
   "source": [
    "## 2.. data cleaning.. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "84f1acca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(119390, 32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64a4ba3f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ee61958f",
   "metadata": {},
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3a0993eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['agent','company'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "687c7ff7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b9fa51b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PRT'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['country'].value_counts().index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "152916e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['country'].fillna(df['country'].value_counts().index[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "131cf9fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8f0b6bd0",
   "metadata": {},
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8becb5c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "### seems to have some dirtiness in data as Adults,babies & children cant be zero at a same time ..\n",
    "\n",
    "### bcz if 3 entities are 0 ,then how can a booking be possible ??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2339aba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Visualise Entire Dataframe where adult,children & babies are 0\n",
    "\n",
    "filter1=(df['children']==0) & (df['adults']==0) & (df['babies']==0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "59249b5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>assigned_room_type</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>deposit_type</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>2224</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>October</td>\n",
       "      <td>41</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>I</td>\n",
       "      <td>1</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient-Party</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>10/6/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2409</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>October</td>\n",
       "      <td>42</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>I</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>10/12/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3181</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>2015</td>\n",
       "      <td>November</td>\n",
       "      <td>47</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient-Party</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>11/23/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3684</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>2015</td>\n",
       "      <td>December</td>\n",
       "      <td>53</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>122</td>\n",
       "      <td>Transient-Party</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>1/4/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3708</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>2015</td>\n",
       "      <td>December</td>\n",
       "      <td>53</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>122</td>\n",
       "      <td>Transient-Party</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>1/5/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115029</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>107</td>\n",
       "      <td>2017</td>\n",
       "      <td>June</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>100.80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>6/30/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115091</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>June</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>K</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>7/1/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116251</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>2017</td>\n",
       "      <td>July</td>\n",
       "      <td>28</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>K</td>\n",
       "      <td>2</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>73.80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>7/17/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116534</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>July</td>\n",
       "      <td>28</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>K</td>\n",
       "      <td>1</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient-Party</td>\n",
       "      <td>22.86</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>7/22/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117087</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>2017</td>\n",
       "      <td>July</td>\n",
       "      <td>30</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>7/29/2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               hotel  is_canceled  lead_time  arrival_date_year  \\\n",
       "2224    Resort Hotel            0          1               2015   \n",
       "2409    Resort Hotel            0          0               2015   \n",
       "3181    Resort Hotel            0         36               2015   \n",
       "3684    Resort Hotel            0        165               2015   \n",
       "3708    Resort Hotel            0        165               2015   \n",
       "...              ...          ...        ...                ...   \n",
       "115029    City Hotel            0        107               2017   \n",
       "115091    City Hotel            0          1               2017   \n",
       "116251    City Hotel            0         44               2017   \n",
       "116534    City Hotel            0          2               2017   \n",
       "117087    City Hotel            0        170               2017   \n",
       "\n",
       "       arrival_date_month  arrival_date_week_number  \\\n",
       "2224              October                        41   \n",
       "2409              October                        42   \n",
       "3181             November                        47   \n",
       "3684             December                        53   \n",
       "3708             December                        53   \n",
       "...                   ...                       ...   \n",
       "115029               June                        26   \n",
       "115091               June                        26   \n",
       "116251               July                        28   \n",
       "116534               July                        28   \n",
       "117087               July                        30   \n",
       "\n",
       "        arrival_date_day_of_month  stays_in_weekend_nights  \\\n",
       "2224                            6                        0   \n",
       "2409                           12                        0   \n",
       "3181                           20                        1   \n",
       "3684                           30                        1   \n",
       "3708                           30                        2   \n",
       "...                           ...                      ...   \n",
       "115029                         27                        0   \n",
       "115091                         30                        0   \n",
       "116251                         15                        1   \n",
       "116534                         15                        2   \n",
       "117087                         27                        0   \n",
       "\n",
       "        stays_in_week_nights  adults  ...  assigned_room_type  \\\n",
       "2224                       3       0  ...                   I   \n",
       "2409                       0       0  ...                   I   \n",
       "3181                       2       0  ...                   C   \n",
       "3684                       4       0  ...                   A   \n",
       "3708                       4       0  ...                   C   \n",
       "...                      ...     ...  ...                 ...   \n",
       "115029                     3       0  ...                   A   \n",
       "115091                     1       0  ...                   K   \n",
       "116251                     1       0  ...                   K   \n",
       "116534                     5       0  ...                   K   \n",
       "117087                     2       0  ...                   A   \n",
       "\n",
       "        booking_changes deposit_type days_in_waiting_list    customer_type  \\\n",
       "2224                  1   No Deposit                    0  Transient-Party   \n",
       "2409                  0   No Deposit                    0        Transient   \n",
       "3181                  0   No Deposit                    0  Transient-Party   \n",
       "3684                  1   No Deposit                  122  Transient-Party   \n",
       "3708                  1   No Deposit                  122  Transient-Party   \n",
       "...                 ...          ...                  ...              ...   \n",
       "115029                1   No Deposit                    0        Transient   \n",
       "115091                0   No Deposit                    0        Transient   \n",
       "116251                2   No Deposit                    0        Transient   \n",
       "116534                1   No Deposit                    0  Transient-Party   \n",
       "117087                0   No Deposit                    0        Transient   \n",
       "\n",
       "           adr  required_car_parking_spaces  total_of_special_requests  \\\n",
       "2224      0.00                            0                          0   \n",
       "2409      0.00                            0                          0   \n",
       "3181      0.00                            0                          0   \n",
       "3684      0.00                            0                          0   \n",
       "3708      0.00                            0                          0   \n",
       "...        ...                          ...                        ...   \n",
       "115029  100.80                            0                          0   \n",
       "115091    0.00                            1                          1   \n",
       "116251   73.80                            0                          0   \n",
       "116534   22.86                            0                          1   \n",
       "117087    0.00                            0                          0   \n",
       "\n",
       "        reservation_status reservation_status_date  \n",
       "2224             Check-Out               10/6/2015  \n",
       "2409             Check-Out              10/12/2015  \n",
       "3181             Check-Out              11/23/2015  \n",
       "3684             Check-Out                1/4/2016  \n",
       "3708             Check-Out                1/5/2016  \n",
       "...                    ...                     ...  \n",
       "115029           Check-Out               6/30/2017  \n",
       "115091           Check-Out                7/1/2017  \n",
       "116251           Check-Out               7/17/2017  \n",
       "116534           Check-Out               7/22/2017  \n",
       "117087           Check-Out               7/29/2017  \n",
       "\n",
       "[180 rows x 30 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[filter1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1b6f7e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=df[~filter1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e603f536",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(119210, 30)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4d4848b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(119390, 30)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e47c1ec1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "aeda710b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>assigned_room_type</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>deposit_type</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>0</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>342</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>7/1/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>737</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>7/1/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>7/2/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>7/2/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>98.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>7/3/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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>A</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</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>9/6/2017</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>E</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</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>9/7/2017</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>D</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</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>9/7/2017</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>A</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</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>9/7/2017</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>A</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</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>9/7/2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110800 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               hotel  is_canceled  lead_time  arrival_date_year  \\\n",
       "0       Resort Hotel            0        342               2015   \n",
       "1       Resort Hotel            0        737               2015   \n",
       "2       Resort Hotel            0          7               2015   \n",
       "3       Resort Hotel            0         13               2015   \n",
       "4       Resort Hotel            0         14               2015   \n",
       "...              ...          ...        ...                ...   \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",
       "0                    July                        27   \n",
       "1                    July                        27   \n",
       "2                    July                        27   \n",
       "3                    July                        27   \n",
       "4                    July                        27   \n",
       "...                   ...                       ...   \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",
       "0                               1                        0   \n",
       "1                               1                        0   \n",
       "2                               1                        0   \n",
       "3                               1                        0   \n",
       "4                               1                        0   \n",
       "...                           ...                      ...   \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  ...  assigned_room_type  \\\n",
       "0                          0       2  ...                   C   \n",
       "1                          0       2  ...                   C   \n",
       "2                          1       1  ...                   C   \n",
       "3                          1       1  ...                   A   \n",
       "4                          2       2  ...                   A   \n",
       "...                      ...     ...  ...                 ...   \n",
       "119385                     5       2  ...                   A   \n",
       "119386                     5       3  ...                   E   \n",
       "119387                     5       2  ...                   D   \n",
       "119388                     5       2  ...                   A   \n",
       "119389                     7       2  ...                   A   \n",
       "\n",
       "        booking_changes deposit_type days_in_waiting_list customer_type  \\\n",
       "0                     3   No Deposit                    0     Transient   \n",
       "1                     4   No Deposit                    0     Transient   \n",
       "2                     0   No Deposit                    0     Transient   \n",
       "3                     0   No Deposit                    0     Transient   \n",
       "4                     0   No Deposit                    0     Transient   \n",
       "...                 ...          ...                  ...           ...   \n",
       "119385                0   No Deposit                    0     Transient   \n",
       "119386                0   No Deposit                    0     Transient   \n",
       "119387                0   No Deposit                    0     Transient   \n",
       "119388                0   No Deposit                    0     Transient   \n",
       "119389                0   No Deposit                    0     Transient   \n",
       "\n",
       "           adr  required_car_parking_spaces  total_of_special_requests  \\\n",
       "0         0.00                            0                          0   \n",
       "1         0.00                            0                          0   \n",
       "2        75.00                            0                          0   \n",
       "3        75.00                            0                          0   \n",
       "4        98.00                            0                          1   \n",
       "...        ...                          ...                        ...   \n",
       "119385   96.14                            0                          0   \n",
       "119386  225.43                            0                          2   \n",
       "119387  157.71                            0                          4   \n",
       "119388  104.40                            0                          0   \n",
       "119389  151.20                            0                          2   \n",
       "\n",
       "        reservation_status reservation_status_date  \n",
       "0                Check-Out                7/1/2015  \n",
       "1                Check-Out                7/1/2015  \n",
       "2                Check-Out                7/2/2015  \n",
       "3                Check-Out                7/2/2015  \n",
       "4                Check-Out                7/3/2015  \n",
       "...                    ...                     ...  \n",
       "119385           Check-Out                9/6/2017  \n",
       "119386           Check-Out                9/7/2017  \n",
       "119387           Check-Out                9/7/2017  \n",
       "119388           Check-Out                9/7/2017  \n",
       "119389           Check-Out                9/7/2017  \n",
       "\n",
       "[110800 rows x 30 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['children']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d25c8275",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ce35500",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9abd6a7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "aa411041",
   "metadata": {},
   "source": [
    "# 3.. Where do the guests come from ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b61e5f50",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Lets perform Spatial Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8b4052bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['is_canceled'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e8f51f82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRT    0.285265\n",
       "GBR    0.128888\n",
       "FRA    0.112890\n",
       "ESP    0.085094\n",
       "DEU    0.080881\n",
       "         ...   \n",
       "BHR    0.000013\n",
       "DJI    0.000013\n",
       "MLI    0.000013\n",
       "NPL    0.000013\n",
       "FRO    0.000013\n",
       "Name: country, Length: 165, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['is_canceled']==0]['country'].value_counts()/75011"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cfea8e59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75011"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data[data['is_canceled']==0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b60581c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>no_of_guests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PRT</td>\n",
       "      <td>21398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GBR</td>\n",
       "      <td>9668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FRA</td>\n",
       "      <td>8468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ESP</td>\n",
       "      <td>6383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DEU</td>\n",
       "      <td>6067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>BHR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>DJI</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>MLI</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>NPL</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>FRO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  no_of_guests\n",
       "0       PRT         21398\n",
       "1       GBR          9668\n",
       "2       FRA          8468\n",
       "3       ESP          6383\n",
       "4       DEU          6067\n",
       "..      ...           ...\n",
       "160     BHR             1\n",
       "161     DJI             1\n",
       "162     MLI             1\n",
       "163     NPL             1\n",
       "164     FRO             1\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_wise_data=data[data['is_canceled']==0]['country'].value_counts().reset_index()\n",
    "country_wise_data.columns=['country','no_of_guests']\n",
    "country_wise_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "35e39e3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "##!pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2261eb29",
   "metadata": {},
   "outputs": [],
   "source": [
    "##!pip install chart_studio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "55e23342",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.18.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly\n",
    "import chart_studio.plotly as py\n",
    "from plotly.offline import download_plotlyjs ,init_notebook_mode ,plot ,iplot\n",
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6beb859b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "32586b8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "map_guest=px.choropleth(country_wise_data,\n",
    "             locations=country_wise_data['country'],\n",
    "             color=country_wise_data['no_of_guests'],\n",
    "              hover_name=country_wise_data['country'],\n",
    "              title='home country of guests'\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d8dda64c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "coloraxis": "coloraxis",
         "geo": "geo",
         "hovertemplate": "<b>%{hovertext}</b><br><br>country=%{location}<br>no_of_guests=%{z}<extra></extra>",
         "hovertext": [
          "PRT",
          "GBR",
          "FRA",
          "ESP",
          "DEU",
          "IRL",
          "ITA",
          "BEL",
          "NLD",
          "USA",
          "BRA",
          "CHE",
          "AUT",
          "CN",
          "SWE",
          "POL",
          "CHN",
          "ISR",
          "NOR",
          "RUS",
          "FIN",
          "ROU",
          "DNK",
          "AUS",
          "LUX",
          "JPN",
          "ARG",
          "AGO",
          "HUN",
          "MAR",
          "TUR",
          "CZE",
          "IND",
          "SRB",
          "GRC",
          "DZA",
          "KOR",
          "MEX",
          "HRV",
          "LTU",
          "NZL",
          "EST",
          "BGR",
          "IRN",
          "ISL",
          "ZAF",
          "CHL",
          "COL",
          "UKR",
          "MOZ",
          "LVA",
          "SVN",
          "SVK",
          "THA",
          "CYP",
          "TWN",
          "MYS",
          "URY",
          "PER",
          "SGP",
          "LBN",
          "EGY",
          "TUN",
          "ECU",
          "JOR",
          "CRI",
          "BLR",
          "PHL",
          "SAU",
          "OMN",
          "IRQ",
          "VEN",
          "KAZ",
          "NGA",
          "MLT",
          "CPV",
          "IDN",
          "CMR",
          "BIH",
          "PRI",
          "ALB",
          "KWT",
          "BOL",
          "PAN",
          "ARE",
          "GNB",
          "MKD",
          "LBY",
          "CUB",
          "AZE",
          "GEO",
          "GIB",
          "LKA",
          "VNM",
          "MUS",
          "ARM",
          "JAM",
          "DOM",
          "CAF",
          "SUR",
          "PAK",
          "GTM",
          "KEN",
          "BRB",
          "CIV",
          "PRY",
          "QAT",
          "SYR",
          "MCO",
          "SEN",
          "HKG",
          "BGD",
          "MNE",
          "MDV",
          "ABW",
          "RWA",
          "SLV",
          "TZA",
          "GAB",
          "TMP",
          "GHA",
          "ATA",
          "LIE",
          "LAO",
          "MWI",
          "ETH",
          "TGO",
          "ZWE",
          "COM",
          "AND",
          "UZB",
          "UGA",
          "STP",
          "KNA",
          "MAC",
          "MRT",
          "BWA",
          "SMR",
          "ZMB",
          "ASM",
          "NCL",
          "GUY",
          "KIR",
          "SDN",
          "ATF",
          "TJK",
          "SLE",
          "CYM",
          "LCA",
          "PYF",
          "BHS",
          "DMA",
          "MMR",
          "AIA",
          "BDI",
          "BFA",
          "PLW",
          "SYC",
          "MDG",
          "NAM",
          "BHR",
          "DJI",
          "MLI",
          "NPL",
          "FRO"
         ],
         "locations": [
          "PRT",
          "GBR",
          "FRA",
          "ESP",
          "DEU",
          "IRL",
          "ITA",
          "BEL",
          "NLD",
          "USA",
          "BRA",
          "CHE",
          "AUT",
          "CN",
          "SWE",
          "POL",
          "CHN",
          "ISR",
          "NOR",
          "RUS",
          "FIN",
          "ROU",
          "DNK",
          "AUS",
          "LUX",
          "JPN",
          "ARG",
          "AGO",
          "HUN",
          "MAR",
          "TUR",
          "CZE",
          "IND",
          "SRB",
          "GRC",
          "DZA",
          "KOR",
          "MEX",
          "HRV",
          "LTU",
          "NZL",
          "EST",
          "BGR",
          "IRN",
          "ISL",
          "ZAF",
          "CHL",
          "COL",
          "UKR",
          "MOZ",
          "LVA",
          "SVN",
          "SVK",
          "THA",
          "CYP",
          "TWN",
          "MYS",
          "URY",
          "PER",
          "SGP",
          "LBN",
          "EGY",
          "TUN",
          "ECU",
          "JOR",
          "CRI",
          "BLR",
          "PHL",
          "SAU",
          "OMN",
          "IRQ",
          "VEN",
          "KAZ",
          "NGA",
          "MLT",
          "CPV",
          "IDN",
          "CMR",
          "BIH",
          "PRI",
          "ALB",
          "KWT",
          "BOL",
          "PAN",
          "ARE",
          "GNB",
          "MKD",
          "LBY",
          "CUB",
          "AZE",
          "GEO",
          "GIB",
          "LKA",
          "VNM",
          "MUS",
          "ARM",
          "JAM",
          "DOM",
          "CAF",
          "SUR",
          "PAK",
          "GTM",
          "KEN",
          "BRB",
          "CIV",
          "PRY",
          "QAT",
          "SYR",
          "MCO",
          "SEN",
          "HKG",
          "BGD",
          "MNE",
          "MDV",
          "ABW",
          "RWA",
          "SLV",
          "TZA",
          "GAB",
          "TMP",
          "GHA",
          "ATA",
          "LIE",
          "LAO",
          "MWI",
          "ETH",
          "TGO",
          "ZWE",
          "COM",
          "AND",
          "UZB",
          "UGA",
          "STP",
          "KNA",
          "MAC",
          "MRT",
          "BWA",
          "SMR",
          "ZMB",
          "ASM",
          "NCL",
          "GUY",
          "KIR",
          "SDN",
          "ATF",
          "TJK",
          "SLE",
          "CYM",
          "LCA",
          "PYF",
          "BHS",
          "DMA",
          "MMR",
          "AIA",
          "BDI",
          "BFA",
          "PLW",
          "SYC",
          "MDG",
          "NAM",
          "BHR",
          "DJI",
          "MLI",
          "NPL",
          "FRO"
         ],
         "name": "",
         "type": "choropleth",
         "z": [
          21398,
          9668,
          8468,
          6383,
          6067,
          2542,
          2428,
          1868,
          1716,
          1592,
          1392,
          1298,
          1033,
          1025,
          793,
          703,
          537,
          500,
          426,
          391,
          377,
          366,
          326,
          319,
          177,
          169,
          160,
          157,
          153,
          150,
          146,
          134,
          116,
          98,
          93,
          82,
          78,
          75,
          75,
          74,
          68,
          65,
          63,
          59,
          53,
          49,
          49,
          48,
          48,
          48,
          46,
          41,
          41,
          41,
          40,
          37,
          25,
          23,
          23,
          22,
          22,
          21,
          20,
          19,
          18,
          18,
          17,
          15,
          15,
          14,
          14,
          14,
          14,
          13,
          13,
          12,
          11,
          10,
          10,
          10,
          10,
          10,
          10,
          9,
          8,
          8,
          8,
          8,
          8,
          8,
          7,
          7,
          7,
          6,
          6,
          6,
          6,
          6,
          5,
          5,
          5,
          4,
          4,
          4,
          4,
          4,
          4,
          3,
          3,
          3,
          3,
          3,
          3,
          3,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          2,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1
         ]
        }
       ],
       "layout": {
        "coloraxis": {
         "colorbar": {
          "title": {
           "text": "no_of_guests"
          }
         },
         "colorscale": [
          [
           0,
           "#0d0887"
          ],
          [
           0.1111111111111111,
           "#46039f"
          ],
          [
           0.2222222222222222,
           "#7201a8"
          ],
          [
           0.3333333333333333,
           "#9c179e"
          ],
          [
           0.4444444444444444,
           "#bd3786"
          ],
          [
           0.5555555555555556,
           "#d8576b"
          ],
          [
           0.6666666666666666,
           "#ed7953"
          ],
          [
           0.7777777777777778,
           "#fb9f3a"
          ],
          [
           0.8888888888888888,
           "#fdca26"
          ],
          [
           1,
           "#f0f921"
          ]
         ]
        },
        "geo": {
         "center": {},
         "domain": {
          "x": [
           0,
           1
          ],
          "y": [
           0,
           1
          ]
         }
        },
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "home country of guests"
        }
       }
      },
      "text/html": [
       "<div>                            <div id=\"95595660-cc10-4e3b-8e5a-6b35191cfd23\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"95595660-cc10-4e3b-8e5a-6b35191cfd23\")) {                    Plotly.newPlot(                        \"95595660-cc10-4e3b-8e5a-6b35191cfd23\",                        [{\"coloraxis\":\"coloraxis\",\"geo\":\"geo\",\"hovertemplate\":\"<b>%{hovertext}</b><br><br>country=%{location}<br>no_of_guests=%{z}<extra></extra>\",\"hovertext\":[\"PRT\",\"GBR\",\"FRA\",\"ESP\",\"DEU\",\"IRL\",\"ITA\",\"BEL\",\"NLD\",\"USA\",\"BRA\",\"CHE\",\"AUT\",\"CN\",\"SWE\",\"POL\",\"CHN\",\"ISR\",\"NOR\",\"RUS\",\"FIN\",\"ROU\",\"DNK\",\"AUS\",\"LUX\",\"JPN\",\"ARG\",\"AGO\",\"HUN\",\"MAR\",\"TUR\",\"CZE\",\"IND\",\"SRB\",\"GRC\",\"DZA\",\"KOR\",\"MEX\",\"HRV\",\"LTU\",\"NZL\",\"EST\",\"BGR\",\"IRN\",\"ISL\",\"ZAF\",\"CHL\",\"COL\",\"UKR\",\"MOZ\",\"LVA\",\"SVN\",\"SVK\",\"THA\",\"CYP\",\"TWN\",\"MYS\",\"URY\",\"PER\",\"SGP\",\"LBN\",\"EGY\",\"TUN\",\"ECU\",\"JOR\",\"CRI\",\"BLR\",\"PHL\",\"SAU\",\"OMN\",\"IRQ\",\"VEN\",\"KAZ\",\"NGA\",\"MLT\",\"CPV\",\"IDN\",\"CMR\",\"BIH\",\"PRI\",\"ALB\",\"KWT\",\"BOL\",\"PAN\",\"ARE\",\"GNB\",\"MKD\",\"LBY\",\"CUB\",\"AZE\",\"GEO\",\"GIB\",\"LKA\",\"VNM\",\"MUS\",\"ARM\",\"JAM\",\"DOM\",\"CAF\",\"SUR\",\"PAK\",\"GTM\",\"KEN\",\"BRB\",\"CIV\",\"PRY\",\"QAT\",\"SYR\",\"MCO\",\"SEN\",\"HKG\",\"BGD\",\"MNE\",\"MDV\",\"ABW\",\"RWA\",\"SLV\",\"TZA\",\"GAB\",\"TMP\",\"GHA\",\"ATA\",\"LIE\",\"LAO\",\"MWI\",\"ETH\",\"TGO\",\"ZWE\",\"COM\",\"AND\",\"UZB\",\"UGA\",\"STP\",\"KNA\",\"MAC\",\"MRT\",\"BWA\",\"SMR\",\"ZMB\",\"ASM\",\"NCL\",\"GUY\",\"KIR\",\"SDN\",\"ATF\",\"TJK\",\"SLE\",\"CYM\",\"LCA\",\"PYF\",\"BHS\",\"DMA\",\"MMR\",\"AIA\",\"BDI\",\"BFA\",\"PLW\",\"SYC\",\"MDG\",\"NAM\",\"BHR\",\"DJI\",\"MLI\",\"NPL\",\"FRO\"],\"locations\":[\"PRT\",\"GBR\",\"FRA\",\"ESP\",\"DEU\",\"IRL\",\"ITA\",\"BEL\",\"NLD\",\"USA\",\"BRA\",\"CHE\",\"AUT\",\"CN\",\"SWE\",\"POL\",\"CHN\",\"ISR\",\"NOR\",\"RUS\",\"FIN\",\"ROU\",\"DNK\",\"AUS\",\"LUX\",\"JPN\",\"ARG\",\"AGO\",\"HUN\",\"MAR\",\"TUR\",\"CZE\",\"IND\",\"SRB\",\"GRC\",\"DZA\",\"KOR\",\"MEX\",\"HRV\",\"LTU\",\"NZL\",\"EST\",\"BGR\",\"IRN\",\"ISL\",\"ZAF\",\"CHL\",\"COL\",\"UKR\",\"MOZ\",\"LVA\",\"SVN\",\"SVK\",\"THA\",\"CYP\",\"TWN\",\"MYS\",\"URY\",\"PER\",\"SGP\",\"LBN\",\"EGY\",\"TUN\",\"ECU\",\"JOR\",\"CRI\",\"BLR\",\"PHL\",\"SAU\",\"OMN\",\"IRQ\",\"VEN\",\"KAZ\",\"NGA\",\"MLT\",\"CPV\",\"IDN\",\"CMR\",\"BIH\",\"PRI\",\"ALB\",\"KWT\",\"BOL\",\"PAN\",\"ARE\",\"GNB\",\"MKD\",\"LBY\",\"CUB\",\"AZE\",\"GEO\",\"GIB\",\"LKA\",\"VNM\",\"MUS\",\"ARM\",\"JAM\",\"DOM\",\"CAF\",\"SUR\",\"PAK\",\"GTM\",\"KEN\",\"BRB\",\"CIV\",\"PRY\",\"QAT\",\"SYR\",\"MCO\",\"SEN\",\"HKG\",\"BGD\",\"MNE\",\"MDV\",\"ABW\",\"RWA\",\"SLV\",\"TZA\",\"GAB\",\"TMP\",\"GHA\",\"ATA\",\"LIE\",\"LAO\",\"MWI\",\"ETH\",\"TGO\",\"ZWE\",\"COM\",\"AND\",\"UZB\",\"UGA\",\"STP\",\"KNA\",\"MAC\",\"MRT\",\"BWA\",\"SMR\",\"ZMB\",\"ASM\",\"NCL\",\"GUY\",\"KIR\",\"SDN\",\"ATF\",\"TJK\",\"SLE\",\"CYM\",\"LCA\",\"PYF\",\"BHS\",\"DMA\",\"MMR\",\"AIA\",\"BDI\",\"BFA\",\"PLW\",\"SYC\",\"MDG\",\"NAM\",\"BHR\",\"DJI\",\"MLI\",\"NPL\",\"FRO\"],\"name\":\"\",\"z\":[21398,9668,8468,6383,6067,2542,2428,1868,1716,1592,1392,1298,1033,1025,793,703,537,500,426,391,377,366,326,319,177,169,160,157,153,150,146,134,116,98,93,82,78,75,75,74,68,65,63,59,53,49,49,48,48,48,46,41,41,41,40,37,25,23,23,22,22,21,20,19,18,18,17,15,15,14,14,14,14,13,13,12,11,10,10,10,10,10,10,9,8,8,8,8,8,8,7,7,7,6,6,6,6,6,5,5,5,4,4,4,4,4,4,3,3,3,3,3,3,3,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],\"type\":\"choropleth\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"geo\":{\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"center\":{}},\"coloraxis\":{\"colorbar\":{\"title\":{\"text\":\"no_of_guests\"}},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"home country of guests\"}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('95595660-cc10-4e3b-8e5a-6b35191cfd23');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "map_guest.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ade2c111",
   "metadata": {},
   "source": [
    "Conclusion : People from all over the world are staying in these two hotels. Most guests are from Portugal and other countries in Europe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbd65bd2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15b37fa8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c5ac68a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c9c1fc87",
   "metadata": {},
   "source": [
    "# 4.. How much do guests pay for a room per night ?"
   ]
  },
  {
   "cell_type": "raw",
   "id": "1f4b650e",
   "metadata": {},
   "source": [
    "Both hotels have different room types and different meal arrangements. Seasonal factors are also important. So the prices vary a lot. Since no currency information is given, but Portugal is part of the European Monetary Union, I assume that all prices are in EUR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b1470228",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2=data[data['is_canceled']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a7ec4af2",
   "metadata": {},
   "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',\n",
       "       '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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "47ef9339",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'price( EUR)')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seaborn boxplot:\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.boxplot(x='reserved_room_type',y='adr' ,hue='hotel',data=data2)\n",
    "\n",
    "plt.title('Price of room types per night and person')\n",
    "plt.xlabel('room types')\n",
    "plt.ylabel('price( EUR)')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "abcb48d9",
   "metadata": {},
   "source": [
    "This figure shows the average price per room, depending on its type and the standard deviation. Note that due to data anonymization rooms with the same type letter may not necessarily be the same across hotels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e61be38",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fae31f51",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4380f5de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d6c5bb8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3473ae65",
   "metadata": {},
   "source": [
    "# 5.. Which are the most busy month ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "158d4c85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Resort Hotel', 'City Hotel'], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['hotel'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "563579ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_resort=data[(data['hotel']=='Resort Hotel') & (data['is_canceled']==0)]\n",
    "data_city = data[(data['hotel']=='City Hotel') & (data['is_canceled']==0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "59662cfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>assigned_room_type</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>deposit_type</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>0</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>342</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>7/1/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>737</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>7/1/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>7/2/2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 30 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",
       "\n",
       "   arrival_date_week_number  arrival_date_day_of_month  \\\n",
       "0                        27                          1   \n",
       "1                        27                          1   \n",
       "2                        27                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  ...  \\\n",
       "0                        0                     0       2  ...   \n",
       "1                        0                     0       2  ...   \n",
       "2                        0                     1       1  ...   \n",
       "\n",
       "   assigned_room_type  booking_changes deposit_type days_in_waiting_list  \\\n",
       "0                   C                3   No Deposit                    0   \n",
       "1                   C                4   No Deposit                    0   \n",
       "2                   C                0   No Deposit                    0   \n",
       "\n",
       "  customer_type   adr  required_car_parking_spaces  total_of_special_requests  \\\n",
       "0     Transient   0.0                            0                          0   \n",
       "1     Transient   0.0                            0                          0   \n",
       "2     Transient  75.0                            0                          0   \n",
       "\n",
       "   reservation_status reservation_status_date  \n",
       "0           Check-Out                7/1/2015  \n",
       "1           Check-Out                7/1/2015  \n",
       "2           Check-Out                7/2/2015  \n",
       "\n",
       "[3 rows x 30 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_resort.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "05fcb692",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>no_of_guests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>August</td>\n",
       "      <td>3257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>July</td>\n",
       "      <td>3137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>October</td>\n",
       "      <td>2575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>March</td>\n",
       "      <td>2571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>April</td>\n",
       "      <td>2550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>May</td>\n",
       "      <td>2535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>February</td>\n",
       "      <td>2308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>September</td>\n",
       "      <td>2102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>June</td>\n",
       "      <td>2037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>December</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>November</td>\n",
       "      <td>1975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>January</td>\n",
       "      <td>1866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        month  no_of_guests\n",
       "0      August          3257\n",
       "1        July          3137\n",
       "2     October          2575\n",
       "3       March          2571\n",
       "4       April          2550\n",
       "5         May          2535\n",
       "6    February          2308\n",
       "7   September          2102\n",
       "8        June          2037\n",
       "9    December          2014\n",
       "10   November          1975\n",
       "11    January          1866"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rush_resort=data_resort['arrival_date_month'].value_counts().reset_index()\n",
    "rush_resort.columns=['month','no_of_guests']\n",
    "rush_resort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4281e8bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>no_of_guests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>August</td>\n",
       "      <td>5367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>July</td>\n",
       "      <td>4770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May</td>\n",
       "      <td>4568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>June</td>\n",
       "      <td>4358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>October</td>\n",
       "      <td>4326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>September</td>\n",
       "      <td>4283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>March</td>\n",
       "      <td>4049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>April</td>\n",
       "      <td>4010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>February</td>\n",
       "      <td>3051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>November</td>\n",
       "      <td>2676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>December</td>\n",
       "      <td>2377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>January</td>\n",
       "      <td>2249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        month  no_of_guests\n",
       "0      August          5367\n",
       "1        July          4770\n",
       "2         May          4568\n",
       "3        June          4358\n",
       "4     October          4326\n",
       "5   September          4283\n",
       "6       March          4049\n",
       "7       April          4010\n",
       "8    February          3051\n",
       "9    November          2676\n",
       "10   December          2377\n",
       "11    January          2249"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rush_city=data_city['arrival_date_month'].value_counts().reset_index()\n",
    "rush_city.columns=['month','no_of_guests']\n",
    "rush_city"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a5b68c12",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9e188f03",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_rush=rush_resort.merge(rush_city,on='month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "94bfc7af",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_rush.columns=['month','no_of_guests_in_resort','no_of_guests_city']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d9363696",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>no_of_guests_in_resort</th>\n",
       "      <th>no_of_guests_city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>August</td>\n",
       "      <td>3257</td>\n",
       "      <td>5367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>July</td>\n",
       "      <td>3137</td>\n",
       "      <td>4770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>October</td>\n",
       "      <td>2575</td>\n",
       "      <td>4326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>March</td>\n",
       "      <td>2571</td>\n",
       "      <td>4049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>April</td>\n",
       "      <td>2550</td>\n",
       "      <td>4010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>May</td>\n",
       "      <td>2535</td>\n",
       "      <td>4568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>February</td>\n",
       "      <td>2308</td>\n",
       "      <td>3051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>September</td>\n",
       "      <td>2102</td>\n",
       "      <td>4283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>June</td>\n",
       "      <td>2037</td>\n",
       "      <td>4358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>December</td>\n",
       "      <td>2014</td>\n",
       "      <td>2377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>November</td>\n",
       "      <td>1975</td>\n",
       "      <td>2676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>January</td>\n",
       "      <td>1866</td>\n",
       "      <td>2249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        month  no_of_guests_in_resort  no_of_guests_city\n",
       "0      August                    3257               5367\n",
       "1        July                    3137               4770\n",
       "2     October                    2575               4326\n",
       "3       March                    2571               4049\n",
       "4       April                    2550               4010\n",
       "5         May                    2535               4568\n",
       "6    February                    2308               3051\n",
       "7   September                    2102               4283\n",
       "8        June                    2037               4358\n",
       "9    December                    2014               2377\n",
       "10   November                    1975               2676\n",
       "11    January                    1866               2249"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_rush"
   ]
  },
  {
   "cell_type": "raw",
   "id": "36d5719f",
   "metadata": {},
   "source": [
    "now we will observe over here is month column is not in order, & if we will visualise we will get improper conclusion\n",
    "so very first we have to provide right hierarchy to the month column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcce6e9f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "579879c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sorted-months-weekdays in c:\\users\\dell\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (0.2)"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip available: 22.3.1 -> 23.1.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Requirement already satisfied: sort_dataframeby_monthorweek in c:\\users\\dell\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (0.4)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip available: 22.3.1 -> 23.1.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "!pip install sorted-months-weekdays\n",
    "\n",
    "## Dependency package needs to be installed\n",
    "!pip install sort_dataframeby_monthorweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "35742f4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sort_dataframeby_monthorweek as sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "19c92da8",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_rush=sd.Sort_Dataframeby_Month(final_rush,'month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b2c4abd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['month', 'no_of_guests_in_resort', 'no_of_guests_city'], dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_rush.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "053c3b81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "variable=no_of_guests_in_resort<br>month=%{x}<br>value=%{y}<extra></extra>",
         "legendgroup": "no_of_guests_in_resort",
         "line": {
          "color": "#636efa",
          "dash": "solid"
         },
         "marker": {
          "symbol": "circle"
         },
         "mode": "lines",
         "name": "no_of_guests_in_resort",
         "orientation": "v",
         "showlegend": true,
         "type": "scatter",
         "x": [
          "January",
          "February",
          "March",
          "April",
          "May",
          "June",
          "July",
          "August",
          "September",
          "October",
          "November",
          "December"
         ],
         "xaxis": "x",
         "y": [
          1866,
          2308,
          2571,
          2550,
          2535,
          2037,
          3137,
          3257,
          2102,
          2575,
          1975,
          2014
         ],
         "yaxis": "y"
        },
        {
         "hovertemplate": "variable=no_of_guests_city<br>month=%{x}<br>value=%{y}<extra></extra>",
         "legendgroup": "no_of_guests_city",
         "line": {
          "color": "#EF553B",
          "dash": "solid"
         },
         "marker": {
          "symbol": "circle"
         },
         "mode": "lines",
         "name": "no_of_guests_city",
         "orientation": "v",
         "showlegend": true,
         "type": "scatter",
         "x": [
          "January",
          "February",
          "March",
          "April",
          "May",
          "June",
          "July",
          "August",
          "September",
          "October",
          "November",
          "December"
         ],
         "xaxis": "x",
         "y": [
          2249,
          3051,
          4049,
          4010,
          4568,
          4358,
          4770,
          5367,
          4283,
          4326,
          2676,
          2377
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "legend": {
         "title": {
          "text": "variable"
         },
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "month"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "value"
         }
        }
       }
      },
      "text/html": [
       "<div>                            <div id=\"a5e20751-4b48-4e4c-afc0-219ffc92eaa5\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"a5e20751-4b48-4e4c-afc0-219ffc92eaa5\")) {                    Plotly.newPlot(                        \"a5e20751-4b48-4e4c-afc0-219ffc92eaa5\",                        [{\"hovertemplate\":\"variable=no_of_guests_in_resort<br>month=%{x}<br>value=%{y}<extra></extra>\",\"legendgroup\":\"no_of_guests_in_resort\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"lines\",\"name\":\"no_of_guests_in_resort\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],\"xaxis\":\"x\",\"y\":[1866,2308,2571,2550,2535,2037,3137,3257,2102,2575,1975,2014],\"yaxis\":\"y\",\"type\":\"scatter\"},{\"hovertemplate\":\"variable=no_of_guests_city<br>month=%{x}<br>value=%{y}<extra></extra>\",\"legendgroup\":\"no_of_guests_city\",\"line\":{\"color\":\"#EF553B\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"lines\",\"name\":\"no_of_guests_city\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],\"xaxis\":\"x\",\"y\":[2249,3051,4049,4010,4568,4358,4770,5367,4283,4326,2676,2377],\"yaxis\":\"y\",\"type\":\"scatter\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"month\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"value\"}},\"legend\":{\"title\":{\"text\":\"variable\"},\"tracegroupgap\":0},\"margin\":{\"t\":60}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('a5e20751-4b48-4e4c-afc0-219ffc92eaa5');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.line(data_frame=final_rush,x='month',y=['no_of_guests_in_resort', 'no_of_guests_city'])"
   ]
  },
  {
   "cell_type": "raw",
   "id": "040672fb",
   "metadata": {},
   "source": [
    "Conclusion-->> This clearly shows that the prices in the Resort hotel are much higher during the summer (no surprise here)., The price of the city hotel varies less and is most expensive during spring and autumn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88d30f41",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d75400d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76a3f4dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35f1c55d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d5d0eea9",
   "metadata": {},
   "source": [
    "# 6.. which month has highest adr ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7b29dc14",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=sd.Sort_Dataframeby_Month(data,'arrival_date_month')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9dfe3574",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='arrival_date_month',y='adr',data=data ,hue='is_canceled')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "139534cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.boxplot(x='arrival_date_month',y='adr',data=data ,hue='is_canceled')\n",
    "plt.xticks(rotation='vertical')\n",
    "\n",
    "\n",
    "plt.ylim(0,800)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f833a44",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25b9c182",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c54f2a06",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2af3012",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8a23b768",
   "metadata": {},
   "source": [
    "# 7.. Lets analyse whether bookings were made only for weekdays or for weekends or for both ??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4b567a61",
   "metadata": {},
   "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',\n",
       "       '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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ac91e51",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b217ef4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>30</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>40</th>\n",
       "      <th>42</th>\n",
       "      <th>50</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>645</td>\n",
       "      <td>16436</td>\n",
       "      <td>17949</td>\n",
       "      <td>11557</td>\n",
       "      <td>4478</td>\n",
       "      <td>830</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4569</td>\n",
       "      <td>7325</td>\n",
       "      <td>8976</td>\n",
       "      <td>6150</td>\n",
       "      <td>2407</td>\n",
       "      <td>1188</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2358</td>\n",
       "      <td>6531</td>\n",
       "      <td>6745</td>\n",
       "      <td>4534</td>\n",
       "      <td>2658</td>\n",
       "      <td>8648</td>\n",
       "      <td>847</td>\n",
       "      <td>446</td>\n",
       "      <td>391</td>\n",
       "      <td>81</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>308</td>\n",
       "      <td>300</td>\n",
       "      <td>397</td>\n",
       "      <td>131</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>347</td>\n",
       "      <td>181</td>\n",
       "      <td>132</td>\n",
       "      <td>86</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "stays_in_week_nights       0      1      2      3     4     5    6    7    8   \\\n",
       "stays_in_weekend_nights                                                         \n",
       "0                         645  16436  17949  11557  4478   830    0    0    0   \n",
       "1                        4569   7325   8976   6150  2407  1188    0    0    0   \n",
       "2                        2358   6531   6745   4534  2658  8648  847  446  391   \n",
       "3                           0      0      0      0     0   308  300  397  131   \n",
       "4                           0      0      0      0     0    94  347  181  132   \n",
       "5                           0      0      0      0     0     0    0    0    0   \n",
       "6                           0      0      0      0     0     0    0    0    0   \n",
       "7                           0      0      0      0     0     0    0    0    0   \n",
       "8                           0      0      0      0     0     0    0    0    0   \n",
       "9                           0      0      0      0     0     0    0    0    0   \n",
       "10                          0      0      0      0     0     0    0    0    0   \n",
       "12                          0      0      0      0     0     0    0    0    0   \n",
       "13                          0      0      0      0     0     0    0    0    0   \n",
       "14                          0      0      0      0     0     0    0    0    0   \n",
       "16                          0      0      0      0     0     0    0    0    0   \n",
       "18                          0      0      0      0     0     0    0    0    0   \n",
       "19                          0      0      0      0     0     0    0    0    0   \n",
       "\n",
       "stays_in_week_nights     9   ...  24  25  26  30  32  33  34  40  42  50  \n",
       "stays_in_weekend_nights      ...                                          \n",
       "0                         0  ...   0   0   0   0   0   0   0   0   0   0  \n",
       "1                         0  ...   0   0   0   0   0   0   0   0   0   0  \n",
       "2                        81  ...   0   0   0   0   0   0   0   0   0   0  \n",
       "3                        61  ...   0   0   0   0   0   0   0   0   0   0  \n",
       "4                        86  ...   0   0   0   0   0   0   0   0   0   0  \n",
       "5                         0  ...   0   0   0   0   0   0   0   0   0   0  \n",
       "6                         0  ...   0   0   0   0   0   0   0   0   0   0  \n",
       "7                         0  ...   0   0   0   0   0   0   0   0   0   0  \n",
       "8                         0  ...   0   0   0   0   0   0   0   0   0   0  \n",
       "9                         0  ...   3   1   0   0   0   0   0   0   0   0  \n",
       "10                        0  ...   0   5   0   0   0   0   0   0   0   0  \n",
       "12                        0  ...   0   0   1   4   0   0   0   0   0   0  \n",
       "13                        0  ...   0   0   0   0   1   1   0   0   0   0  \n",
       "14                        0  ...   0   0   0   0   0   0   1   0   0   0  \n",
       "16                        0  ...   0   0   0   0   0   0   0   2   0   0  \n",
       "18                        0  ...   0   0   0   0   0   0   0   0   1   0  \n",
       "19                        0  ...   0   0   0   0   0   0   0   0   0   1  \n",
       "\n",
       "[17 rows x 33 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Lets create a relationship table..\n",
    "pd.crosstab(index=data['stays_in_weekend_nights'],columns=data['stays_in_week_nights'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98dfc031",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f8c62b06",
   "metadata": {},
   "outputs": [],
   "source": [
    "## lets define our own function :\n",
    "\n",
    "def week_function(row):\n",
    "    feature1='stays_in_weekend_nights'\n",
    "    feature2='stays_in_week_nights'\n",
    "    \n",
    "    if row[feature2]==0 and row[feature1] >0 :\n",
    "        return 'stay_just_weekend'\n",
    "    \n",
    "    elif row[feature2]>0 and row[feature1] ==0 :\n",
    "        return 'stay_just_weekdays'\n",
    "    \n",
    "    elif row[feature2]>0 and row[feature1] >0 :\n",
    "        return 'stay_both_weekdays_weekends'\n",
    "    \n",
    "    else:\n",
    "        return 'undefined_data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3902444e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2['weekend_or_weekday']=data2.apply(week_function,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d3c6c64b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>booking_changes</th>\n",
       "      <th>deposit_type</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",
       "      <th>weekend_or_weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>342</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>7/1/2015</td>\n",
       "      <td>undefined_data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>737</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>7/1/2015</td>\n",
       "      <td>undefined_data</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 31 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",
       "\n",
       "   arrival_date_week_number  arrival_date_day_of_month  \\\n",
       "0                        27                          1   \n",
       "1                        27                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  ...  \\\n",
       "0                        0                     0       2  ...   \n",
       "1                        0                     0       2  ...   \n",
       "\n",
       "   booking_changes  deposit_type days_in_waiting_list customer_type  adr  \\\n",
       "0                3    No Deposit                    0     Transient  0.0   \n",
       "1                4    No Deposit                    0     Transient  0.0   \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",
       "\n",
       "   reservation_status_date weekend_or_weekday  \n",
       "0                 7/1/2015     undefined_data  \n",
       "1                 7/1/2015     undefined_data  \n",
       "\n",
       "[2 rows x 31 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f3c02545",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stay_both_weekdays_weekends    37551\n",
       "stay_just_weekdays             31788\n",
       "stay_just_weekend               5050\n",
       "undefined_data                   622\n",
       "Name: weekend_or_weekday, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2['weekend_or_weekday'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a8967595",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "module"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(sd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "76576ce1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2=sd.Sort_Dataframeby_Month(data2,'arrival_date_month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3ac3177a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "arrival_date_month  weekend_or_weekday         \n",
       "April               stay_both_weekdays_weekends    3627\n",
       "                    stay_just_weekdays             2559\n",
       "                    stay_just_weekend               344\n",
       "                    undefined_data                   30\n",
       "August              stay_both_weekdays_weekends    4929\n",
       "                    stay_just_weekdays             3137\n",
       "                    stay_just_weekend               509\n",
       "                    undefined_data                   49\n",
       "December            stay_both_weekdays_weekends    1901\n",
       "                    stay_just_weekdays             2123\n",
       "                    stay_just_weekend               298\n",
       "                    undefined_data                   69\n",
       "February            stay_both_weekdays_weekends    2438\n",
       "                    stay_just_weekdays             2514\n",
       "                    stay_just_weekend               360\n",
       "                    undefined_data                   47\n",
       "January             stay_both_weekdays_weekends    1550\n",
       "                    stay_just_weekdays             2125\n",
       "                    stay_just_weekend               393\n",
       "                    undefined_data                   47\n",
       "July                stay_both_weekdays_weekends    4570\n",
       "                    stay_just_weekdays             2818\n",
       "                    stay_just_weekend               462\n",
       "                    undefined_data                   57\n",
       "June                stay_both_weekdays_weekends    3241\n",
       "                    stay_just_weekdays             2685\n",
       "                    stay_just_weekend               433\n",
       "                    undefined_data                   36\n",
       "March               stay_both_weekdays_weekends    3151\n",
       "                    stay_just_weekdays             3060\n",
       "                    stay_just_weekend               359\n",
       "                    undefined_data                   50\n",
       "May                 stay_both_weekdays_weekends    3442\n",
       "                    stay_just_weekdays             3017\n",
       "                    stay_just_weekend               570\n",
       "                    undefined_data                   74\n",
       "November            stay_both_weekdays_weekends    2117\n",
       "                    stay_just_weekdays             2214\n",
       "                    stay_just_weekend               261\n",
       "                    undefined_data                   59\n",
       "October             stay_both_weekdays_weekends    3393\n",
       "                    stay_just_weekdays             2844\n",
       "                    stay_just_weekend               582\n",
       "                    undefined_data                   82\n",
       "September           stay_both_weekdays_weekends    3192\n",
       "                    stay_just_weekdays             2692\n",
       "                    stay_just_weekend               479\n",
       "                    undefined_data                   22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.groupby(['arrival_date_month','weekend_or_weekday']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3171f7a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "group_data=data2.groupby(['arrival_date_month','weekend_or_weekday']).size().unstack().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7514dadd",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_data=sd.Sort_Dataframeby_Month(group_data,'arrival_date_month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e777b19e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_data.set_index('arrival_date_month',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "62582408",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stay_both_weekdays_weekends</th>\n",
       "      <th>stay_just_weekdays</th>\n",
       "      <th>stay_just_weekend</th>\n",
       "      <th>undefined_data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>1550</td>\n",
       "      <td>2125</td>\n",
       "      <td>393</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>2438</td>\n",
       "      <td>2514</td>\n",
       "      <td>360</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>3151</td>\n",
       "      <td>3060</td>\n",
       "      <td>359</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>3627</td>\n",
       "      <td>2559</td>\n",
       "      <td>344</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>3442</td>\n",
       "      <td>3017</td>\n",
       "      <td>570</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>3241</td>\n",
       "      <td>2685</td>\n",
       "      <td>433</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>4570</td>\n",
       "      <td>2818</td>\n",
       "      <td>462</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>4929</td>\n",
       "      <td>3137</td>\n",
       "      <td>509</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>3192</td>\n",
       "      <td>2692</td>\n",
       "      <td>479</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>3393</td>\n",
       "      <td>2844</td>\n",
       "      <td>582</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>2117</td>\n",
       "      <td>2214</td>\n",
       "      <td>261</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>1901</td>\n",
       "      <td>2123</td>\n",
       "      <td>298</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    stay_both_weekdays_weekends  stay_just_weekdays  \\\n",
       "arrival_date_month                                                    \n",
       "January                                    1550                2125   \n",
       "February                                   2438                2514   \n",
       "March                                      3151                3060   \n",
       "April                                      3627                2559   \n",
       "May                                        3442                3017   \n",
       "June                                       3241                2685   \n",
       "July                                       4570                2818   \n",
       "August                                     4929                3137   \n",
       "September                                  3192                2692   \n",
       "October                                    3393                2844   \n",
       "November                                   2117                2214   \n",
       "December                                   1901                2123   \n",
       "\n",
       "                    stay_just_weekend  undefined_data  \n",
       "arrival_date_month                                     \n",
       "January                           393              47  \n",
       "February                          360              47  \n",
       "March                             359              50  \n",
       "April                             344              30  \n",
       "May                               570              74  \n",
       "June                              433              36  \n",
       "July                              462              57  \n",
       "August                            509              49  \n",
       "September                         479              22  \n",
       "October                           582              82  \n",
       "November                          261              59  \n",
       "December                          298              69  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3387bc01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='arrival_date_month'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABMsAAAOACAYAAAAkVfSDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/P9b71AAAACXBIWXMAAA9hAAAPYQGoP6dpAACVTUlEQVR4nOzdebhVBb3/8c9hHg+EIkMig4Ji4ogpooKC4ZA3w3LipiBYllRojldFUHPKWSvLCsxETHPK2VRwgFAxBQXRCMVuICoC4cB4fn+43D/PxRQE3Qyv1/Oc52Hvtfba370WkbxZa+2KqqqqqgAAAAAAqVHuAQAAAABgbSGWAQAAAEBBLAMAAACAglgGAAAAAAWxDAAAAAAKYhkAAAAAFMQyAAAAACiIZQAAAABQqFXuAT4vy5cvz7/+9a80btw4FRUV5R4HAAAAgDKpqqrKv//977Ru3To1anzyuWPrbSz717/+lTZt2pR7DAAAAADWEq+99lo23XTTT1xnvY1ljRs3TvLBTqisrCzzNAAAAACUy4IFC9KmTZtSL/ok620s+/DSy8rKSrEMAAAAgJW6VZcb/AMAAABAQSwDAAAAgIJYBgAAAACF9faeZQAAAKz7li9fnsWLF5d7DGAdUKdOndSosfrnhYllAAAArJUWL16cGTNmZPny5eUeBVgH1KhRI+3bt0+dOnVWaztiGQAAAGudqqqqzJo1KzVr1kybNm3WyNkiwPpr+fLl+de//pVZs2Zls802W6lvvfxPxDIAAADWOkuXLs27776b1q1bp0GDBuUeB1gHNG/ePP/617+ydOnS1K5d+zNvR5oHAABgrbNs2bIkWe3LqYANx4d/Xnz458dnJZYBAACw1lqdS6mADcua+vNCLAMAAACAglgGAAAA66kxY8akoqIi8+bNW+PbHjlyZJo2bbrGt/t5+Sz74pVXXklFRUWeffbZz22utVm7du1y+eWXf+HvW1FRkdtvv/0Lf98PucE/AAAA64x2p979hb7fKxccsMa21b9//8ybN6+sEeCzateuXYYMGZIhQ4aUexT43DmzDAAAAAAKYhkAAACsQbfccku6dOmS+vXrZ6ONNkrv3r1z0kkn5brrrssdd9yRioqKVFRUZMyYMUmSU045JZ06dUqDBg3SoUOHnHnmmVmyZEmSDy4DrFGjRp5++ulq73H55Zenbdu2Wb58+UrN9MQTT2TbbbdNvXr1suuuu+b555+vtvxPf/pTvvKVr6Ru3bpp165dLrnkktKynj175tVXX83xxx9fmv2j7r///nTu3DmNGjXKvvvum1mzZn3qPM8//3xq1KiRN954I0kyd+7c1KhRI4cddlhpnXPPPTe77757tdfst99+adSoUVq0aJHvfOc7efPNN0vLly9fnvPPPz/t27dP/fr1s9122+WWW275jzO8++672W+//dK9e/fSpZlPPvlkdthhh9SrVy9du3bN3/72t2qvWbZsWQYOHFh6jy233DJXXHFFafmjjz6a2rVrZ/bs2dVeN2TIkOyxxx5JkldffTUHHnhgvvSlL6Vhw4b5yle+knvuuedT91nXrl1z8cUXlx4fdNBBqV27dhYuXJgk+ec//5mKior8/e9/T5IsWrQoJ554Yr785S+nYcOG2WWXXUq/5z70+OOPZ4899kj9+vXTpk2b/OhHP8o777zzH2f4zW9+k6ZNm+ahhx5K8unHpGfPnvnRj36Uk08+Oc2aNUvLli0zbNiwatt8+eWXs+eee6ZevXrZeuut8+CDD1Zbvnjx4gwePDitWrVKvXr10rZt25x//vmfur9Wh1gGAAAAa8isWbNy+OGH5+ijj87UqVMzZsyY9O3bN2eddVYOOeSQUkyaNWtWdttttyRJ48aNM3LkyEyZMiVXXHFFrr322lx22WVJPrj8sXfv3hkxYkS19xkxYkT69++fGjVW7q/1J510Ui655JI89dRTad68eQ488MBSkJs4cWIOOeSQHHbYYZk8eXKGDRuWM888MyNHjkyS3Hrrrdl0001z9tlnl2b/0LvvvpuLL744119/fR599NHMnDkzJ5544qfO85WvfCUbbbRRxo4dmyR57LHHqj1OkrFjx6Znz55Jknnz5mXvvffODjvskKeffjr33XdfXn/99RxyyCGl9c8///z8/ve/zzXXXJMXXnghxx9/fP77v/+72jY/NG/evOyzzz5Zvnx5HnzwwTRt2jQLFy7M17/+9Wy99daZOHFihg0btsJnWb58eTbddNPcfPPNmTJlSoYOHZr/+Z//yR//+MckyZ577pkOHTrk+uuvL71myZIlueGGG3L00UcnSY477rgsWrQojz76aCZPnpwLL7wwjRo1+tR91qNHj1LsqqqqymOPPZamTZvm8ccfL+2vL3/5y9liiy2SJIMHD8748eMzevToTJo0Kd/+9rez77775uWXX06STJ8+Pfvuu28OPvjgTJo0KTfddFMef/zxDB48+GPf/6KLLsqpp56aBx54IL169VqpY5Ik1113XRo2bJgJEybkoosuytlnn10KYsuXL0/fvn1Tp06dTJgwIddcc01OOeWUaq+/8sorc+edd+aPf/xjpk2blhtuuCHt2rX71P21OtyzDAAAANaQWbNmZenSpenbt2/atm2bJOnSpUuSpH79+lm0aFFatmxZ7TVnnHFG6dft2rXLiSeemNGjR+fkk09OkgwaNCjHHntsLr300tStWzfPPPNMJk+enDvuuGOl5zrrrLOyzz77JPkgXmy66aa57bbbcsghh+TSSy9Nr169cuaZZyZJOnXqlClTpuRnP/tZ+vfvn2bNmqVmzZpp3LjxCrMvWbIk11xzTTbffPMkHwSas88++1PnqaioyJ577pkxY8bkW9/6VsaMGZMBAwbkN7/5TV588cVsvvnmGTduXGkfXH311dlhhx1y3nnnlbbxu9/9Lm3atMlLL72Utm3b5rzzzstf/vKXdOvWLUnSoUOHPP744/nVr36VHj16lF43e/bsHHrooenYsWNGjRqVOnXqJElGjRqV5cuX57e//W3q1auXr3zlK/nnP/+Z73//+6XX1q5dO8OHDy89bt++fcaPH58//vGPpUg0cODAjBgxIieddFKS5M9//nPef//90vKZM2fm4IMPLv2+6NChw6fur+SDs7R++9vfZtmyZXn++edTp06dHHrooRkzZkz23XffjBkzpvQ5Z86cmREjRmTmzJlp3bp1kuTEE0/MfffdlxEjRuS8887L+eefn379+pXuQ9exY8dceeWV6dGjR375y1+mXr16pfc+5ZRTcv3112fs2LH5yle+slLHpFOnTkmSbbfdNmeddVbpPa6++uo89NBD2WefffKXv/wlL774Yu6///7SnOedd17222+/0jZnzpyZjh07Zvfdd09FRUXpf1efJ2eWAQAAwBqy3XbbpVevXunSpUu+/e1v59prr83bb7/9ia+56aab0r1797Rs2TKNGjXKGWeckZkzZ5aWH3TQQalZs2Zuu+22JB98C+Vee+21SmfXfBiQkqRZs2bZcsstM3Xq1CTJ1KlT071792rrd+/ePS+//HKWLVv2idtt0KBBKZQlSatWrTJnzpyVmumjZ0qNHTs2e++9dymgPfXUU1myZElprueeey6PPPJIGjVqVPrZaqutknxwhtTf//73vPvuu9lnn32qrfP73/8+06dPr/a+++yzT7bYYovcdNNNpVD24X748FLVj9tvH/r5z3+enXbaKc2bN0+jRo3y61//utrx6t+/f/7+97/nr3/9a5IPjtchhxyShg0bJkl+9KMf5dxzz0337t1z1llnZdKkSSu1v/bYY4/8+9//zt/+9reMHTs2PXr0SM+ePavtww/PxJs8eXKWLVuWTp06VdsfY8eOLe2P5557LiNHjqy2vE+fPlm+fHlmzJhRet9LLrkk1157bR5//PFSKFuZY/Khbbfdttrn+OjvkalTp6ZNmzalUPZx+7x///559tlns+WWW+ZHP/pRHnjggZXaX6tDLAMAAIA1pGbNmnnwwQdz7733Zuutt85VV12VLbfcslp8+Kjx48enX79+2X///XPXXXflb3/7W04//fQsXry4tE6dOnVy5JFHZsSIEVm8eHFGjRpVuqSv3GrXrl3tcUVFRaqqqlbqtT179syUKVPy8ssvZ8qUKdl9991L8Wfs2LHp2rVrGjRokCRZuHBhDjzwwDz77LPVfj6839WH9+26++67qy2fMmXKCvctO+CAA/Loo49mypQpq/x5R48enRNPPDEDBw7MAw88kGeffTYDBgyodrw22WSTHHjggRkxYkRef/313HvvvdWO16BBg/KPf/wj3/nOdzJ58uR07do1V1111ae+d9OmTbPddtuV9k/Pnj2z55575m9/+1teeumlvPzyy6UzyxYuXJiaNWtm4sSJ1fbH1KlTS/dYW7hwYb73ve9VW/7cc8/l5ZdfrhZA99hjjyxbtqx0qemHPu2YfOjjfo+s7L32kmTHHXfMjBkzcs455+S9997LIYcckm9961sr/frPwmWYAAAAsAZVVFSke/fu6d69e4YOHZq2bdvmtttuS506dVY4U2vcuHFp27ZtTj/99NJzr7766grbHDRoULbZZpv84he/KF3muSr++te/ZrPNNkuSvP3223nppZfSuXPnJEnnzp3zxBNPVFv/iSeeSKdOnVKzZs0k+djZV1eXLl3ypS99Keeee2623377NGrUKD179syFF16Yt99+u3SWVPJBMPnTn/6Udu3apVatFVPG1ltvnbp162bmzJnVLrn8OBdccEEaNWqUXr16ZcyYMdl6662TfLAfrr/++rz//vuls8s+PDvsQ0888UR22223/OAHPyg993/PXEs+OF6HH354Nt1002y++eYrnLnXpk2bHHvssTn22GNz2mmn5dprr80Pf/jDT95h+eBsvEceeSRPPvlkfvrTn6ZZs2bp3LlzfvrTn6ZVq1alSx932GGHLFu2LHPmzCl9scD/teOOO2bKlCmle5z9J1/96lczePDg7LvvvqlVq1bpPm6fdkxWRufOnfPaa69l1qxZadWqVZIV93mSVFZW5tBDD82hhx6ab33rW9l3330zd+7cNGvW7DO976dxZhkAAACsIRMmTMh5552Xp59+OjNnzsytt96aN954I507d067du0yadKkTJs2LW+++WaWLFmSjh07ZubMmRk9enSmT5+eK6+8snS55Ud17tw5u+66a0455ZQcfvjhqV+//irNdfbZZ+ehhx7K888/n/79+2fjjTfOQQcdlCT5yU9+koceeijnnHNOXnrppVx33XW5+uqrq93cvl27dnn00Ufzv//7v9W+7XB1fHjfshtuuKEUxrbddtssWrQoDz30ULXoddxxx2Xu3Lk5/PDD89RTT2X69Om5//77M2DAgCxbtiyNGzfOiSeemOOPPz7XXXddpk+fnmeeeSZXXXVVrrvuuhXe++KLL06/fv2y995758UXX0ySHHHEEamoqMgxxxyTKVOm5J577qn27ZPJB/fcevrpp3P//ffnpZdeyplnnpmnnnpqhe336dMnlZWVOffcczNgwIBqy4YMGZL7778/M2bMyDPPPJNHHnmkFC4/Tc+ePXP//fenVq1apUsee/bsmRtuuKHa/urUqVP69euXI488MrfeemtmzJiRJ598Mueff37uvvvuJB/ch2zcuHEZPHhw6YywO+6442Nv8L/bbrvlnnvuyfDhw3P55Zev1DFZGb17906nTp1y1FFH5bnnnstjjz1WLRwnyaWXXpobb7wxL774Yl566aXcfPPNadmyZZo2bbpS7/GZVK2n5s+fX5Wkav78+eUeBQAAgFX03nvvVU2ZMqXqvffeK/coq2TKlClVffr0qWrevHlV3bp1qzp16lR11VVXVVVVVVXNmTOnap999qlq1KhRVZKqRx55pKqqqqrqpJNOqtpoo42qGjVqVHXooYdWXXbZZVVNmjRZYdu//e1vq5JUPfnkkys9zyOPPFKVpOrPf/5z1Ve+8pWqOnXqVH31q1+teu6556qtd8stt1RtvfXWVbVr167abLPNqn72s59VWz5+/Piqbbfdtqpu3bpVH6aEESNGrDDnbbfdVrUqqeGyyy6rSlJ17733lp77xje+UVWrVq2qf//739XWfemll6q++c1vVjVt2rSqfv36VVtttVXVkCFDqpYvX15VVVVVtXz58qrLL7+8asstt6yqXbt2VfPmzav69OlTNXbs2Gr74u233y5t84c//GFVq1atqqZNm1b6nNttt11VnTp1qrbffvuqP/3pT1VJqv72t79VVVVVVb3//vtV/fv3r2rSpElV06ZNq77//e9XnXrqqVXbbbfdCp/tzDPPrKpZs2bVv/71r2rPDx48uGrzzTevqlu3blXz5s2rvvOd71S9+eabK7W/3nrrraqKioqqQw89tPTch/v8mmuuqbbu4sWLq4YOHVrVrl27qtq1a1e1atWq6pvf/GbVpEmTSus8+eSTpd+TDRs2rNp2222rfvrTn5aWt23btuqyyy4rPR47dmxVw4YNq6688sqVOiY9evSo+vGPf1xtrm984xtVRx11VOnxtGnTqnbfffeqOnXqVHXq1Knqvvvuq0pSddttt1VVVVVV/frXv67afvvtqxo2bFhVWVlZ1atXr6pnnnnmY/fPJ/25sSqdqKKqaiUvJl7HLFiwIE2aNMn8+fNTWVlZ7nEAAABYBe+//35mzJiR9u3bV7vh+obsnHPOyc0337zSN4SnvAYOHJg33ngjd955Z7lH2WB80p8bq9KJ3LMMAAAA1mILFy7MK6+8kquvvjrnnntuucfhU8yfPz+TJ0/OqFGjhLJ1lHuWAQAAwFps8ODB2WmnndKzZ88VvgXz2GOPTaNGjT7259hjjy3TxB/4T3M1atQojz32WFln+zx94xvfyNe+9rUce+yx2WeffVb6dWvzsdzQuAwTAACAtY7LMFfOnDlzsmDBgo9dVllZmU022eQLnuj/+/vf//4fl335y19e5S8pWN+tzcdyXeEyTAAAANjAbbLJJmttRNliiy3KPcI6ZW0+lhsal2ECAAAAQEEsAwAAAICCWAYAAAAABbEMAAAAAApiGQAAAAAUxDIAAABYT73yyiupqKjIs88+W+5RvjDDhg3L9ttvv0qvGTlyZJo2bfq5zMO6p1a5BwAAAICVNqzJF/x+89fYpvr375958+bl9ttvX2Pb/DRt2rTJrFmzsvHGG6+R7Y0cOTJDhgzJvHnz1sj2YG0klgEAwDpu6ladyz3Cauv84tRyjwDrpZo1a6Zly5blHgPWKS7DBAAAgDXolltuSZcuXVK/fv1stNFG6d27d0466aRcd911ueOOO1JRUZGKioqMGTMmSXLKKaekU6dOadCgQTp06JAzzzwzS5YsSfLBZZQ1atTI008/Xe09Lr/88rRt2zbLly//xFn+72WYH3e54e23356KiorS4+eeey577bVXGjdunMrKyuy00055+umnM2bMmAwYMCDz588vfYZhw4Z94vtfffXV2WabbVZ4r2uuuab0XO/evXPGGWeUHt9xxx3ZcccdU69evXTo0CHDhw/P0qVLS8vnzZuXQYMGpXnz5qmsrMzee++d55577j/OMH369HTo0CGDBw9OVVVVaT9sttlmadCgQb75zW/mrbfeWuE13/jGN9KiRYs0atQoO++8c/7yl7+Ulp999tnVPteHtt9++5x55plJkjFjxuSrX/1qGjZsmKZNm6Z79+559dVXP3F/sXYQywAAAGANmTVrVg4//PAcffTRmTp1asaMGZO+ffvmrLPOyiGHHJJ99903s2bNyqxZs7LbbrslSRo3bpyRI0dmypQpueKKK3LttdfmsssuS5K0a9cuvXv3zogRI6q9z4gRI9K/f//UqLHm/1rfr1+/bLrppnnqqacyceLEnHrqqaldu3Z22223XH755amsrCx9hhNPPPETt9WjR49MmTIlb7zxRpJk7Nix2XjjjUuhcMmSJRk/fnx69uyZJHnsscdy5JFH5sc//nGmTJmSX/3qVxk5cmR++tOflrb57W9/O3PmzMm9996biRMnZscdd0yvXr0yd+7cFd5/0qRJ2X333XPEEUfk6quvTkVFRSZMmJCBAwdm8ODBefbZZ7PXXnvl3HPPrfa6hQsXZv/9989DDz2Uv/3tb9l3331z4IEHZubMmUlSOr5PPfVU6TV/+9vfMmnSpAwYMCBLly7NQQcdlB49emTSpEkZP358vvvd71aLkqy9XIYJAAAAa8isWbOydOnS9O3bN23btk2SdOnSJUlSv379LFq0aIXLIj96VlW7du1y4oknZvTo0Tn55JOTJIMGDcqxxx6bSy+9NHXr1s0zzzyTyZMn54477vhcPsPMmTNz0kknZauttkqSdOzYsbSsSZMmqaioWOlLO7fZZps0a9YsY8eOzbe+9a2MGTMmP/nJT3LFFVckSZ588sksWbKkFA6HDx+eU089NUcddVSSpEOHDjnnnHNy8skn56yzzsrjjz+eJ598MnPmzEndunWTJBdffHFuv/323HLLLfnud79beu9x48bl61//ek4//fT85Cc/KT1/xRVXZN999y3t306dOmXcuHG57777Sutst9122W677UqPzznnnNx222258847M3jw4Gy66abp06dPRowYkZ133jnJBwGzR48e6dChQ+bOnZv58+fn61//ejbffPMkSefO6/4l8xsKZ5YBAADAGrLddtulV69e6dKlS7797W/n2muvzdtvv/2Jr7npppvSvXv3tGzZMo0aNcoZZ5xROoMpSQ466KDUrFkzt912W5IPLiHca6+90q5du8/lM5xwwgkZNGhQevfunQsuuCDTp0//zNuqqKjInnvumTFjxmTevHmZMmVKfvCDH2TRokV58cUXM3bs2Oy8885p0KBBkg8uAT377LPTqFGj0s8xxxyTWbNm5d13381zzz2XhQsXZqONNqq2zowZM6rNOXPmzOyzzz4ZOnRotVCWJFOnTs0uu+xS7blu3bpVe7xw4cKceOKJ6dy5c5o2bZpGjRpl6tSp1Y7LMccckxtvvDHvv/9+Fi9enFGjRuXoo49OkjRr1iz9+/dPnz59cuCBB+aKK67IrFmzPvN+5IvlzDIAAFjHHXLauv+f9ZPLPQCsITVr1syDDz6YcePG5YEHHshVV12V008/PRMmTPjY9cePH59+/fpl+PDh6dOnT5o0aZLRo0fnkksuKa1Tp06dHHnkkRkxYkT69u2bUaNGlc7MWlU1atQo3bfrQx/eH+1Dw4YNyxFHHJG777479957b84666yMHj063/zmNz/Te/bs2TO//vWv89hjj2WHHXZIZWVlKaCNHTs2PXr0KK27cOHCDB8+PH379l1hO/Xq1cvChQvTqlWr0mWcH/XRe7E1b948rVu3zo033pijjz46lZWVqzTziSeemAcffDAXX3xxtthii9SvXz/f+ta3snjx4tI6Bx54YOrWrZvbbrstderUyZIlS/Ktb32rtHzEiBH50Y9+lPvuuy833XRTzjjjjDz44IPZddddV2kWvnjr/v+rAgAAwFqkoqIi3bt3T/fu3TN06NC0bdu2FFSWLVtWbd1x48albdu2Of3000vPfdxN4AcNGpRtttkmv/jFL0qXeX4WzZs3z7///e+88847adiwYZKUbv7/UZ06dUqnTp1y/PHH5/DDD8+IESPyzW9+82M/w6fp0aNHhgwZkptvvrl0b7KePXvmL3/5S5544olqZ37tuOOOmTZtWrbYYouP3daOO+6Y2bNnp1atWp94Zl39+vVz1113Zf/990+fPn3ywAMPpHHjxkk+uBzy/8bLv/71r9UeP/HEE+nfv38pEC5cuDCvvPJKtXVq1aqVo446KiNGjEidOnVy2GGHpX79+tXW2WGHHbLDDjvktNNOS7du3TJq1CixbB3gMkwAAABYQyZMmJDzzjsvTz/9dGbOnJlbb701b7zxRjp37px27dpl0qRJmTZtWt58880sWbIkHTt2zMyZMzN69OhMnz49V155Zelyy4/q3Llzdt1115xyyik5/PDDV4gyK2uXXXZJgwYN8j//8z+ZPn16Ro0alZEjR5aWv/feexk8eHDGjBmTV199NU888USeeuqp0v222rVrl4ULF+ahhx7Km2++mXffffdT33PbbbfNl770pYwaNapaLLv99tuzaNGidO/evbTu0KFD8/vf/z7Dhw/PCy+8kKlTp2b06NGl+7r17t073bp1y0EHHZQHHnggr7zySsaNG5fTTz99hW8MbdiwYe6+++7UqlUr++23XxYuXJgkpbO9Lr744rz88su5+uqrq92vLPngPm233nprnn322Tz33HM54ogjPvabRwcNGpSHH3449913X+kSzCSZMWNGTjvttIwfPz6vvvpqHnjggbz88svuW7aOcGYZAAAA645h88s9wSeqrKzMo48+mssvvzwLFixI27Ztc8kll2S//fZL165dM2bMmHTt2jULFy7MI488kv/6r//K8ccfn8GDB2fRokU54IADcuaZZ2bYsGErbHvgwIEZN25ctSizqpo1a5Y//OEPOemkk3LttdemV69eGTZsWOnG+DVr1sxbb72VI488Mq+//no23njj9O3bN8OHD0+S7Lbbbjn22GNz6KGH5q233spZZ531sbN+VEVFRfbYY4/cfffd2X333ZN8ENAqKyuz5ZZbls5wS5I+ffrkrrvuytlnn50LL7wwtWvXzlZbbZVBgwaVtnXPPffk9NNPz4ABA/LGG2+kZcuW2XPPPdOiRYsV3rtRo0a5995706dPnxxwwAG55557suuuu+baa6/NWWedlaFDh6Z3794544wzcs4555Red+mll+boo4/Obrvtlo033jinnHJKFixYsML2O3bsmN122y1z586tdh+0Bg0a5MUXX8x1112Xt956K61atcpxxx2X733veyt5pCiniqr/e7HyemLBggVp0qRJ5s+fv8rXJgMAwLqky3Vdyj3Capt8lLuWUd3777+fGTNmpH379qlXr165x1krnHPOObn55pszadKklX7NtGnTstVWW+Xll1/+j5c28tlVVVWlY8eO+cEPfpATTjih3ONs8D7pz41V6UTOLAMAAIC12If3y7r66qtz7rnnrvTr5s6dm1tuuSWVlZVp06bN5zjhhumNN97I6NGjM3v27AwYMKDc47AGiWUAAACwFhs8eHBuvPHGHHTQQStcgnnsscfmD3/4w8e+buedd8706dPzy1/+MnXr1v1cZnvsscey3377/cflH94nbH20ySabZOONN86vf/3rfOlLXyr3OKxBLsMEAIB1nMswWR+5DHPlzJkz52PvpZV8cP+0TTbZ5HN9//feey//+7//+x+Xu/STL5LLMAEAAGADt8kmm3zuQeyT1K9fXxBjvVOj3AMAAAAAwNpCLAMAAACAglgGAAAAAAWxDAAAAAAKYhkAAAAAFMQyAAAAWE+98sorqaioyLPPPlvuUb4ww4YNy/bbb/+Fv2///v1z0EEHfeHvy5pXq9wDAAAAwMrqcl2XL/T9Jh81eY1tq3///pk3b15uv/32NbbNT9OmTZvMmjUrG2+88RrZ3siRIzNkyJDMmzdvjWwP1kZiGQAAAKynatasmZYtW5Z7DFinuAwTAAAA1qBbbrklXbp0Sf369bPRRhuld+/eOemkk3LdddfljjvuSEVFRSoqKjJmzJgkySmnnJJOnTqlQYMG6dChQ84888wsWbIkyQeXUdaoUSNPP/10tfe4/PLL07Zt2yxfvvwTZ/m/l2GOHDkyTZs2rbbO7bffnoqKitLj5557LnvttVcaN26cysrK7LTTTnn66aczZsyYDBgwIPPnzy99hmHDhn3i+1999dXZZpttVniva665pvRc7969c8YZZ5Qe33HHHdlxxx1Tr169dOjQIcOHD8/SpUtLy+fNm5dBgwalefPmqayszN57753nnnvuP84wffr0dOjQIYMHD05VVVUWLVqUE088MV/+8pfTsGHD7LLLLqVj8dF9dP/996dz585p1KhR9t1338yaNau0zrJly3LCCSekadOm2WijjXLyySenqqrqE/cF6w6xDAAAANaQWbNm5fDDD8/RRx+dqVOnZsyYMenbt2/OOuusHHLIIaXoMmvWrOy2225JksaNG2fkyJGZMmVKrrjiilx77bW57LLLkiTt2rVL7969M2LEiGrvM2LEiPTv3z81aqz5v9b369cvm266aZ566qlMnDgxp556amrXrp3ddtstl19+eSorK0uf4cQTT/zEbfXo0SNTpkzJG2+8kSQZO3ZsNt5441KcWrJkScaPH5+ePXsmSR577LEceeSR+fGPf5wpU6bkV7/6VUaOHJmf/vSnpW1++9vfzpw5c3Lvvfdm4sSJ2XHHHdOrV6/MnTt3hfefNGlSdt999xxxxBG5+uqrU1FRkcGDB2f8+PEZPXp0Jk2alG9/+9vZd9998/LLL5de9+677+biiy/O9ddfn0cffTQzZ86s9lkvueSSjBw5Mr/73e/y+OOPZ+7cubnttts+6y5nLSOWAQAAwBoya9asLF26NH379k27du3SpUuX/OAHP0ijRo1Sv3791K1bNy1btkzLli1Tp06dJMkZZ5yR3XbbLe3atcuBBx6YE088MX/84x9L2xw0aFBuvPHGLFq0KEnyzDPPZPLkyRkwYMDn8hlmzpyZ3r17Z6uttkrHjh3z7W9/O9ttt13q1KmTJk2apKKiovQZGjVq9Inb2mabbdKsWbOMHTs2STJmzJj85Cc/KT1+8skns2TJklI4HD58eE499dQcddRR6dChQ/bZZ5+cc845+dWvfpUkefzxx/Pkk0/m5ptvTteuXdOxY8dcfPHFadq0aW655ZZq7z1u3Lj07NkzJ554Ys4999zSZxsxYkRuvvnm7LHHHtl8881z4oknZvfdd68WJJcsWZJrrrkmXbt2zY477pjBgwfnoYceKi2//PLLc9ppp6Vv377p3LlzrrnmmjRp0mQ19zxrC7EMAAAA1pDtttsuvXr1SpcuXfLtb3871157bd5+++1PfM1NN92U7t27l+LTGWeckZkzZ5aWH3TQQalZs2bpzKWRI0dmr732Srt27T6Xz3DCCSdk0KBB6d27dy644IJMnz79M2+roqIie+65Z8aMGZN58+ZlypQp+cEPfpBFixblxRdfzNixY7PzzjunQYMGST64BPTss89Oo0aNSj/HHHNMZs2alXfffTfPPfdcFi5cmI022qjaOjNmzKg258yZM7PPPvtk6NCh+clPflJ6fvLkyVm2bFk6depU7fVjx46t9voGDRpk8803Lz1u1apV5syZkySZP39+Zs2alV122aW0vFatWunatetn3k+sXdzgHwAAANaQmjVr5sEHH8y4cePywAMP5Kqrrsrpp5+eCRMmfOz648ePT79+/TJ8+PD06dMnTZo0yejRo3PJJZeU1qlTp06OPPLIjBgxIn379s2oUaNyxRVXfKb5atSoscK9tT68P9qHhg0bliOOOCJ333137r333px11lkZPXp0vvnNb36m9+zZs2d+/etf57HHHssOO+yQysrKUkAbO3ZsevToUVp34cKFGT58ePr27bvCdurVq5eFCxemVatW1e4x9qGP3outefPmad26dW688cYcffTRqaysLG2/Zs2amThxYmrWrFnt9R89S6527drVllVUVLgn2QZELAMAAIA1qKKiIt27d0/37t0zdOjQtG3bNrfddlvq1KmTZcuWVVt33Lhxadu2bU4//fTSc6+++uoK2xw0aFC22Wab/OIXvyhd5vlZNG/ePP/+97/zzjvvpGHDhklSuvn/R3Xq1CmdOnXK8ccfn8MPPzwjRozIN7/5zY/9DJ+mR48eGTJkSG6++ebSvcl69uyZv/zlL3niiSeqnfm14447Ztq0adliiy0+dls77rhjZs+enVq1an3imXX169fPXXfdlf333z99+vTJAw88kMaNG2eHHXbIsmXLMmfOnOyxxx6r9Dk+1KRJk7Rq1SoTJkzInnvumSRZunRp6f5prPtchgkAAABryIQJE3Leeefl6aefzsyZM3PrrbfmjTfeSOfOndOuXbtMmjQp06ZNy5tvvpklS5akY8eOmTlzZkaPHp3p06fnyiuv/NgbxXfu3Dm77rprTjnllBx++OGpX7/+Z5pvl112SYMGDfI///M/mT59ekaNGpWRI0eWlr/33nsZPHhwxowZk1dffTVPPPFEnnrqqXTu3DnJB184sHDhwjz00EN588038+67737qe2677bb50pe+lFGjRlWLZbfffnsWLVqU7t27l9YdOnRofv/732f48OF54YUXMnXq1IwePbr0bZm9e/dOt27dctBBB+WBBx7IK6+8knHjxuX0009f4RtDGzZsmLvvvju1atXKfvvtl4ULF6ZTp07p169fjjzyyNx6662ZMWNGnnzyyZx//vm5++67V3o//vjHP84FF1yQ22+/PS+++GJ+8IMfZN68eSv9etZuYhkAAACsIZWVlXn00Uez//77p1OnTjnjjDNyySWXZL/99ssxxxyTLbfcMl27dk3z5s3zxBNP5L/+679y/PHHZ/Dgwdl+++0zbty4nHnmmR+77YEDB2bx4sU5+uijP/N8zZo1yx/+8Ifcc8896dKlS2688cYMGzastLxmzZp56623cuSRR6ZTp0455JBDst9++2X48OFJkt122y3HHntsDj300DRv3jwXXXTRp75nRUVF9thjj1RUVGT33XdP8kFAq6ysTNeuXUtnuCVJnz59ctddd+WBBx7IzjvvnF133TWXXXZZ2rZtW9rWPffckz333DMDBgxIp06dcthhh+XVV19NixYtVnjvRo0a5d57701VVVUOOOCAvPPOOxkxYkSOPPLI/OQnP8mWW26Zgw46KE899VQ222yzld6PP/nJT/Kd73wnRx11VLp165bGjRt/5stUWftUVK2nF90uWLAgTZo0yfz580vXJgMAwPqoy3Vdyj3Capt81ORyj8Ba5v3338+MGTPSvn371KtXr9zjrBXOOeec3HzzzZk0adJKv2batGnZaqut8vLLL//HSxthffFJf26sSidyZhkAAACsxRYuXJjnn38+V199dX74wx+u9Ovmzp2bW265JZWVlWnTps3nOCGsX9zgHwAAANZigwcPzo033piDDjpohUswjz322PzhD3/42NftvPPOmT59en75y1+mbt26n8tsjz32WPbbb7//uHzhwoWfy/vC58llmAAAsI5zGSbrI5dhrpw5c+ZkwYIFH7ussrIym2yyyef6/u+9917+93//9z8ud+knX6Q1dRmmM8sAAABgHbXJJpt87kHsk9SvX18QY73jnmUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAADWYmPGjElFRUXmzZu30q959913c/DBB6eysrL02nbt2uXyyy//3Ob8UM+ePTNkyJDP/Povak74T2qVewAAAABYWVO36vyFvl/nF6d+oe+3plx33XV57LHHMm7cuGy88cZp0qRJnnrqqTRs2LDco61xI0eOzJAhQ1YpJsInEcsAAABgPTN9+vR07tw522yzTem55s2bl3EiWHe4DBMAAADWkI+7hHD77bfPsGHDkiQVFRX5zW9+k29+85tp0KBBOnbsmDvvvLPa+vfcc086deqU+vXrZ6+99sorr7yywvs8/vjj2WOPPVK/fv20adMmP/rRj/LOO+8k+eAyyEsuuSSPPvpoKioq0rNnz4+dbWVmef7557PffvulUaNGadGiRb7zne/kzTffLC1/5513cuSRR6ZRo0Zp1apVLrnkklXaX3PmzMmBBx6Y+vXrp3379rnhhhtWWOfSSy9Nly5d0rBhw7Rp0yY/+MEPsnDhwiQfXKI6YMCAzJ8/PxUVFamoqCjt6+uvvz5du3ZN48aN07JlyxxxxBGZM2fOKs3HhkksAwAAgC/Q8OHDc8ghh2TSpEnZf//9069fv8ydOzdJ8tprr6Vv37458MAD8+yzz2bQoEE59dRTq71++vTp2XfffXPwwQdn0qRJuemmm/L4449n8ODBSZJbb701xxxzTLp165ZZs2bl1ltv/UyzzJs3L3vvvXd22GGHPP3007nvvvvy+uuv55BDDim9/qSTTsrYsWNzxx135IEHHsiYMWPyzDPPrPS+6N+/f1577bU88sgjueWWW/KLX/xihaBVo0aNXHnllXnhhRdy3XXX5eGHH87JJ5+cJNltt91y+eWXp7KyMrNmzcqsWbNy4oknJkmWLFmSc845J88991xuv/32vPLKK+nfv/9Kz8aGy2WYAAAA8AXq379/Dj/88CTJeeedlyuvvDJPPvlk9t133/zyl7/M5ptvXjpDa8stt8zkyZNz4YUXll5//vnnp1+/fqWb6Hfs2DFXXnllevTokV/+8pdp1qxZGjRokDp16qRly5afeZarr746O+ywQ84777zS+r/73e/Spk2bvPTSS2ndunV++9vf5g9/+EN69eqV5IN7pW266aYrtR9eeuml3HvvvXnyySez8847J0l++9vfpnPn6vel++iXBbRr1y7nnntujj322PziF79InTp10qRJk1RUVKzwWY8++ujSrzt06JArr7wyO++8cxYuXJhGjRqt1IxsmMQyAAAA+AJtu+22pV83bNgwlZWVpbOppk6dml122aXa+t26dav2+LnnnsukSZOqXbJYVVWV5cuXZ8aMGSvEps86y3PPPZdHHnnkY8PS9OnT895772Xx4sXV5m3WrFm23HLLlXrvqVOnplatWtlpp51Kz2211VZp2rRptfX+8pe/5Pzzz8+LL76YBQsWZOnSpXn//ffz7rvvpkGDBv9x+xMnTsywYcPy3HPP5e23387y5cuTJDNnzszWW2+9UjOyYRLLAAAAYA2pUaNGqqqqqj23ZMmSao9r165d7XFFRUUp5KyMhQsX5nvf+15+9KMfrbBss802W4VpP3mWhQsX5sADD6x2VtuHWrVqlb///e+r9F6fxSuvvJKvf/3r+f73v5+f/vSnadasWR5//PEMHDgwixcv/o+x7J133kmfPn3Sp0+f3HDDDWnevHlmzpyZPn36ZPHixZ/73KzbxDIAAFjHTZ4xs9wjAIXmzZtn1qxZpccLFizIjBkzVvr1nTt3XuEm+3/961+rPd5xxx0zZcqUbLHFFqs37KfYcccd86c//Snt2rVLrVor5oPNN988tWvXzoQJE0qR7u23385LL72UHj16fOr2t9pqqyxdujQTJ04sXYY5bdq0zJs3r7TOxIkTs3z58lxyySWpUeOD267/8Y9/rLadOnXqZNmyZdWee/HFF/PWW2/lggsuSJs2bZIkTz/99Mp/eDZoq3SD/2XLluXMM89M+/btU79+/Wy++eY555xzqlXzqqqqDB06NK1atUr9+vXTu3fvvPzyy9W2M3fu3PTr1y+VlZVp2rRpBg4cWPomiw9NmjQpe+yxR+rVq5c2bdrkoosuWo2PCQAAAJ+/vffeO9dff30ee+yxTJ48OUcddVRq1qy50q8/9thj8/LLL+ekk07KtGnTMmrUqIwcObLaOqecckrGjRuXwYMH59lnn83LL7+cO+64o3SD/zXluOOOy9y5c3P44YfnqaeeyvTp03P//fdnwIABWbZsWRo1apSBAwfmpJNOysMPP5znn38+/fv3L0WtT7Pllltm3333zfe+971MmDAhEydOzKBBg1K/fv3SOltssUWWLFmSq666Kv/4xz9y/fXX55prrqm2nXbt2mXhwoV56KGH8uabb+bdd9/NZpttljp16pRed+edd+acc85Zo/uH9dcqxbILL7wwv/zlL3P11Vdn6tSpufDCC3PRRRflqquuKq1z0UUX5corr8w111yTCRMmpGHDhunTp0/ef//90jr9+vXLCy+8kAcffDB33XVXHn300Xz3u98tLV+wYEG+9rWvpW3btpk4cWJ+9rOfZdiwYfn1r3+9Bj4yAAAAfD5OO+209OjRI1//+tdzwAEH5KCDDsrmm2++0q/fbLPN8qc//Sm33357tttuu1xzzTXVbrCffHCfsbFjx+all17KHnvskR122CFDhw5N69at1+hnad26dZ544oksW7YsX/va19KlS5cMGTIkTZs2LQWxn/3sZ9ljjz1y4IEHpnfv3tl9992r3YPs04wYMSKtW7dOjx490rdv33z3u9/NJptsUlq+3Xbb5dJLL82FF16YbbbZJjfccEPOP//8atvYbbfdcuyxx+bQQw9N8+bNc9FFF6V58+YZOXJkbr755my99da54IILcvHFF6+ZHcN6r6Lq/15M/Qm+/vWvp0WLFvntb39beu7ggw9O/fr184c//CFVVVVp3bp1fvKTn5S+qnX+/Plp0aJFRo4cmcMOOyxTp07N1ltvnaeeeipdu3ZNktx3333Zf//9889//jOtW7fOL3/5y5x++umZPXt26tSpkyQ59dRTc/vtt+fFF19cqVkXLFiQJk2aZP78+amsrFzpHQIAAOucYU3KPcHqGza/3BOwlnn//fczY8aMtG/fPvXq1Sv3OMA64JP+3FiVTrRKZ5bttttueeihh/LSSy8l+eCbMR5//PHst99+SZIZM2Zk9uzZ6d27d+k1TZo0yS677JLx48cnScaPH5+mTZuWQlmS9O7dOzVq1MiECRNK6+y5556lUJYkffr0ybRp0/L222+vysgAAAAAsNJW6Qb/p556ahYsWJCtttoqNWvWzLJly/LTn/40/fr1S5LMnj07SdKiRYtqr2vRokVp2ezZs6udUpkktWrVSrNmzaqt0759+xW28eGyL33pSyvMtmjRoixatKj0eMGCBavy0QAAAIA16LHHHiudXPNx/u+9y2FtsUqx7I9//GNuuOGGjBo1Kl/5ylfy7LPPZsiQIWndunWOOuqoz2vGlXL++edn+PDhZZ0BAAAA+EDXrl3z7LPPlnsMWGWrFMtOOumknHrqqTnssMOSJF26dMmrr76a888/P0cddVRatmyZJHn99dfTqlWr0utef/31bL/99kmSli1bZs6cOdW2u3Tp0sydO7f0+pYtW+b111+vts6Hjz9c5/867bTTcsIJJ5QeL1iwoPT1sAAAAMAXq379+tliiy3KPQasslW6Z9m77767wlfA1qxZM8uXL0+StG/fPi1btsxDDz1UWr5gwYJMmDAh3bp1S5J069Yt8+bNy8SJE0vrPPzww1m+fHl22WWX0jqPPvpolixZUlrnwQcfzJZbbvmxl2AmSd26dVNZWVntBwAAAABWxSrFsgMPPDA//elPc/fdd+eVV17JbbfdlksvvTTf/OY3kyQVFRUZMmRIzj333Nx5552ZPHlyjjzyyLRu3ToHHXRQkqRz587Zd999c8wxx+TJJ5/ME088kcGDB+ewww4rfc3tEUcckTp16mTgwIF54YUXctNNN+WKK66oduYYAAAA67+qqqpyjwCsI9bUnxerdBnmVVddlTPPPDM/+MEPMmfOnLRu3Trf+973MnTo0NI6J598ct55551897vfzbx587L77rvnvvvuq/aVnTfccEMGDx6cXr16pUaNGjn44INz5ZVXlpY3adIkDzzwQI477rjstNNO2XjjjTN06NB897vfXQMfGQAAgLVd7dq1U1FRkTfeeCPNmzdPRUVFuUcC1mJVVVV54403UlFRkdq1a6/Wtiqq1tNMv2DBgjRp0iTz5893SSYAAOu3YU3KPcHqGza/3BOwFlq4cGH++c9/OrsMWCkVFRXZdNNN06hRoxWWrUonWqUzywAAAOCL0qhRo3Ts2LHa/awB/pPatWunZs2aq70dsQwAgNUydavO5R5htXV+cWq5RwD+g5o1a66Rv/wCrKxVusE/AAAAAKzPxDIAAAAAKIhlAAAAAFAQywAAAACg4Ab/AACslkNOW/f/k3JyuQcAANYaziwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKBQq9wDAACwbps8Y2a5RwAAWGOcWQYAAAAABbEMAAAAAApiGQAAAAAUxDIAAAAAKIhlAAAAAFAQywAAAACgIJYBAAAAQEEsAwAAAICCWAYAAAAABbEMAAAAAApiGQAAAAAUxDIAAAAAKIhlAAAAAFAQywAAAACgIJYBAAAAQEEsAwAAAICCWAYAAAAABbEMAAAAAApiGQAAAAAUxDIAAAAAKIhlAAAAAFAQywAAAACgIJYBAAAAQKFWuQcAgHXd1K06l3uE1dL5xanlHgEAANYaziwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAo+DZMAFhNh5y2bv/f6eRyDwAAAGsRZ5YBAAAAQEEsAwAAAICCWAYAAAAABbEMAAAAAApiGQAAAAAUxDIAAAAAKIhlAAAAAFAQywAAAACgIJYBAAAAQEEsAwAAAIBCrXIPAAAAAKtr6ladyz3Caun84tRyjwAUnFkGAAAAAAWxDAAAAAAKYhkAAAAAFMQyAAAAACiIZQAAAABQEMsAAAAAoFCr3AMAAADA6jrktHX7r7eTyz0AUOLMMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABRqlXsAYN02davO5R5htXR+cWq5RwAAAGAt4swyAAAAACiIZQAAAABQEMsAAAAAoOCeZcBqOeS0dfuPkcnlHgAAAIC1yrr9t1wAfMkCAADAGuQyTAAAAAAoiGUAAAAAUHAZJsA6zn3jAAAA1hxnlgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTW7a9QA4C1wOQZM8s9AgAAsIY4swwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUHCDfwBgnTZ1q87lHmG1dX5xarlHAACgIJYBAACsJuEeYP3hMkwAAAAAKDizDABYpx1y2rr/nzOTyz0AsNr8WQSw/nBmGQAAAAAUxDIAAAAAKIhlAAAAAFAQywAAAACgIJYBAAAAQEEsAwAAAICCWAYAAAAABbEMAAAAAApiGQAAAAAUapV7AAAAAFhdk2fMLPcIwHrCmWUAAAAAUBDLAAAAAKDgMkxgtTjdHQAAgPWJM8sAAAAAoODMMoB1nLP7AAAA1hxnlgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgMIqx7L//d//zX//939no402Sv369dOlS5c8/fTTpeVVVVUZOnRoWrVqlfr166d37955+eWXq21j7ty56devXyorK9O0adMMHDgwCxcurLbOpEmTsscee6RevXpp06ZNLrroos/4EQEAAABg5axSLHv77bfTvXv31K5dO/fee2+mTJmSSy65JF/60pdK61x00UW58sorc80112TChAlp2LBh+vTpk/fff7+0Tr9+/fLCCy/kwQcfzF133ZVHH3003/3ud0vLFyxYkK997Wtp27ZtJk6cmJ/97GcZNmxYfv3rX6+BjwwAAAAAH6/Wqqx84YUXpk2bNhkxYkTpufbt25d+XVVVlcsvvzxnnHFGvvGNbyRJfv/736dFixa5/fbbc9hhh2Xq1Km577778tRTT6Vr165Jkquuuir7779/Lr744rRu3To33HBDFi9enN/97nepU6dOvvKVr+TZZ5/NpZdeWi2qAQAAAMCatEpnlt15553p2rVrvv3tb2eTTTbJDjvskGuvvba0fMaMGZk9e3Z69+5deq5JkybZZZddMn78+CTJ+PHj07Rp01IoS5LevXunRo0amTBhQmmdPffcM3Xq1Cmt06dPn0ybNi1vv/32Z/ukAAAAAPApVimW/eMf/8gvf/nLdOzYMffff3++//3v50c/+lGuu+66JMns2bOTJC1atKj2uhYtWpSWzZ49O5tsskm15bVq1UqzZs2qrfNx2/joe/xfixYtyoIFC6r9AAAAAMCqWKXLMJcvX56uXbvmvPPOS5LssMMOef7553PNNdfkqKOO+lwGXFnnn39+hg8fXtYZAAAAAFi3rdKZZa1atcrWW29d7bnOnTtn5syZSZKWLVsmSV5//fVq67z++uulZS1btsycOXOqLV+6dGnmzp1bbZ2P28ZH3+P/Ou200zJ//vzSz2uvvbYqHw0AAAAAVi2Wde/ePdOmTav23EsvvZS2bdsm+eBm/y1btsxDDz1UWr5gwYJMmDAh3bp1S5J069Yt8+bNy8SJE0vrPPzww1m+fHl22WWX0jqPPvpolixZUlrnwQcfzJZbblntmzc/qm7duqmsrKz2AwAAAACrYpVi2fHHH5+//vWvOe+88/L3v/89o0aNyq9//escd9xxSZKKiooMGTIk5557bu68885Mnjw5Rx55ZFq3bp2DDjooyQdnou2777455phj8uSTT+aJJ57I4MGDc9hhh6V169ZJkiOOOCJ16tTJwIED88ILL+Smm27KFVdckRNOOGHNfnoAAAAA+IhVumfZzjvvnNtuuy2nnXZazj777LRv3z6XX355+vXrV1rn5JNPzjvvvJPvfve7mTdvXnbffffcd999qVevXmmdG264IYMHD06vXr1So0aNHHzwwbnyyitLy5s0aZIHHnggxx13XHbaaadsvPHGGTp0aL773e+ugY8MAAAAAB9vlWJZknz961/P17/+9f+4vKKiImeffXbOPvvs/7hOs2bNMmrUqE98n2233TaPPfbYqo4HAAAAAJ/ZKscyAIC1yeQZM8s9AgAA65FVumcZAAAAAKzPxDIAAAAAKLgMEwAAYDW5JBxg/eHMMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAoVa5B4DVMXWrzuUeYbV0fnFquUcAAAAAPsKZZQAAAABQEMsAAAAAoCCWAQAAAEDBPctYpx1y2rr9W3hyuQcAAAAAqnFmGQAAAAAUxDIAAAAAKIhlAAAAAFAQywAAAACgIJYBAAAAQEEsAwAAAICCWAYAAAAABbEMAAAAAApiGQAAAAAUxDIAAAAAKIhlAAAAAFAQywAAAACgIJYBAAAAQEEsAwAAAICCWAYAAAAABbEMAAAAAApiGQAAAAAUxDIAAAAAKIhlAAAAAFAQywAAAACgIJYBAAAAQEEsAwAAAICCWAYAAAAABbEMAAAAAApiGQAAAAAUxDIAAAAAKIhlAAAAAFAQywAAAACgIJYBAAAAQEEsAwAAAIBCrXIPAKtj8oyZ5R4BAAAAWI84swwAAAAACs4sAwCAdVy790eVe4TV9kq5BwCAgjPLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABRqlXsAAAAAYN03davO5R5htXR+cWq5R2At4cwyAAAAACiIZQAAAABQEMsAAAAAoCCWAQAAAEDBDf4BAACA1XbIaet2Yphc7gFYazizDAAAAAAK63b2BQCg7Nq9P6rcI6y2V8o9AACw1nBmGQAAAAAUxDIAAAAAKIhlAAAAAFAQywAAAACgIJYBAAAAQEEsAwAAAICCWAYAAAAABbEMAAAAAApiGQAAAAAUxDIAAAAAKIhlAAAAAFAQywAAAACgIJYBAAAAQEEsAwAAAIBCrXIPAAAAAMDqm7pV53KPsFo6vzi13CMkcWYZAAAAAJSIZQAAAABQEMsAAAAAoCCWAQAAAEBBLAMAAACAgm/DBAAAAFbb5Bkzyz3CBu+Q09btzDO53AMUnFkGAAAAAAWxDAAAAAAKYhkAAAAAFMQyAAAAACiIZQAAAABQEMsAAAAAoCCWAQAAAEBBLAMAAACAglgGAAAAAAWxDAAAAAAKYhkAAAAAFMQyAAAAACiIZQAAAABQEMsAAAAAoCCWAQAAAEBBLAMAAACAglgGAAAAAIVa5R5gXTZ1q87lHmG1dX5xarlHAAAAAFhrOLMMAAAAAApiGQAAAAAUxDIAAAAAKLhn2Wo45LR1f/dNLvcAAAAAAGuR1Tqz7IILLkhFRUWGDBlSeu7999/Pcccdl4022iiNGjXKwQcfnNdff73a62bOnJkDDjggDRo0yCabbJKTTjopS5curbbOmDFjsuOOO6Zu3brZYostMnLkyNUZFQAAAAA+1WeOZU899VR+9atfZdttt632/PHHH58///nPufnmmzN27Nj861//St++fUvLly1blgMOOCCLFy/OuHHjct1112XkyJEZOnRoaZ0ZM2bkgAMOyF577ZVnn302Q4YMyaBBg3L//fd/1nEBAAAA4FN9pli2cOHC9OvXL9dee22+9KUvlZ6fP39+fvvb3+bSSy/N3nvvnZ122ikjRozIuHHj8te//jVJ8sADD2TKlCn5wx/+kO233z777bdfzjnnnPz85z/P4sWLkyTXXHNN2rdvn0suuSSdO3fO4MGD861vfSuXXXbZGvjIAAAAAPDxPtNNt4477rgccMAB6d27d84999zS8xMnTsySJUvSu3fv0nNbbbVVNttss4wfPz677rprxo8fny5duqRFixaldfr06ZPvf//7eeGFF7LDDjtk/Pjx1bbx4TofvdwTAAAAgP9v8oyZ5R5hvbDKsWz06NF55pln8tRTT62wbPbs2alTp06aNm1a7fkWLVpk9uzZpXU+Gso+XP7hsk9aZ8GCBXnvvfdSv379Fd570aJFWbRoUenxggULVvWjAQAAALCBW6XLMF977bX8+Mc/zg033JB69ep9XjN9Jueff36aNGlS+mnTpk25RwIAAABgHbNKsWzixImZM2dOdtxxx9SqVSu1atXK2LFjc+WVV6ZWrVpp0aJFFi9enHnz5lV73euvv56WLVsmSVq2bLnCt2N++PjT1qmsrPzYs8qS5LTTTsv8+fNLP6+99tqqfDQAAAAAWLVY1qtXr0yePDnPPvts6adr167p169f6de1a9fOQw89VHrNtGnTMnPmzHTr1i1J0q1bt0yePDlz5swprfPggw+msrIyW2+9dWmdj27jw3U+3MbHqVu3biorK6v9AAAAAMCqWKV7ljVu3DjbbLNNtecaNmyYjTbaqPT8wIEDc8IJJ6RZs2aprKzMD3/4w3Tr1i277rprkuRrX/tatt5663znO9/JRRddlNmzZ+eMM87Icccdl7p16yZJjj322Fx99dU5+eSTc/TRR+fhhx/OH//4x9x9991r4jMDAAAAwMf6TN+G+Ukuu+yy1KhRIwcffHAWLVqUPn365Be/+EVpec2aNXPXXXfl+9//frp165aGDRvmqKOOytlnn11ap3379rn77rtz/PHH54orrsimm26a3/zmN+nTp8+aHhcAAAAASlY7lo0ZM6ba43r16uXnP/95fv7zn//H17Rt2zb33HPPJ263Z8+e+dvf/ra64wEAAADASlule5YBAAAAwPpMLAMAAACAglgGAAAAAAWxDAAAAAAKYhkAAAAAFMQyAAAAACiIZQAAAABQEMsAAAAAoCCWAQAAAEBBLAMAAACAglgGAAAAAAWxDAAAAAAKYhkAAAAAFMQyAAAAACiIZQAAAABQEMsAAAAAoCCWAQAAAEBBLAMAAACAglgGAAAAAAWxDAAAAAAKYhkAAAAAFGqVe4B12eQZM8s9AgAAAABrkDPLAAAAAKAglgEAAABAQSwDAAAAgIJ7lgGs49q9P6rcI6yWV8o9AAAAwEc4swwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgEKtcg8AAOu6du+PKvcIq+WVcg8AAABrEWeWAQAAAEBBLAMAAACAglgGAAAAAAX3LAMAAGCd5x6iwJrizDIAAAAAKIhlAAAAAFAQywAAAACgIJYBAAAAQEEsAwAAAICCWAYAAAAABbEMAAAAAApiGQAAAAAUxDIAAAAAKIhlAAAAAFAQywAAAACgIJYBAAAAQKFWuQcAAFgd7d4fVe4RVtsr5R4AAIASZ5YBAAAAQEEsAwAAAICCWAYAAAAABfcsAwAAWE3unwiw/nBmGQAAAAAUxDIAAAAAKIhlAAAAAFBwzzJgtazr9+d4pdwDAAAAsFZxZhkAAAAAFMQyAAAAACiIZQAAAABQEMsAAAAAoCCWAQAAAEBBLAMAAACAglgGAAAAAAWxDAAAAAAKYhkAAAAAFMQyAAAAACiIZQAAAABQEMsAAAAAoCCWAQAAAEBBLAMAAACAglgGAAAAAAWxDAAAAAAKYhkAAAAAFMQyAAAAACiIZQAAAABQEMsAAAAAoCCWAQAAAEBBLAMAAACAglgGAAAAAAWxDAAAAAAKYhkAAAAAFMQyAAAAACiIZQAAAABQEMsAAAAAoCCWAQAAAEBBLAMAAACAglgGAAAAAAWxDAAAAAAKYhkAAAAAFMQyAAAAACiIZQAAAABQEMsAAAAAoCCWAQAAAEBBLAMAAACAglgGAAAAAAWxDAAAAAAKYhkAAAAAFMQyAAAAACiIZQAAAABQEMsAAAAAoCCWAQAAAEBBLAMAAACAglgGAAAAAAWxDAAAAAAKYhkAAAAAFMQyAAAAACiIZQAAAABQEMsAAAAAoCCWAQAAAEBBLAMAAACAglgGAAAAAAWxDAAAAAAKYhkAAAAAFMQyAAAAACiIZQAAAABQEMsAAAAAoFCr3APA6mj3/qhyj7BaXin3AAAAAEA1ziwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAACFWuUeAAAAAFj3tXt/VLlHWC2vlHsA1hrOLAMAAACAglgGAAAAAAWxDAAAAAAKYhkAAAAAFFYplp1//vnZeeed07hx42yyySY56KCDMm3atGrrvP/++znuuOOy0UYbpVGjRjn44IPz+uuvV1tn5syZOeCAA9KgQYNssskmOemkk7J06dJq64wZMyY77rhj6tatmy222CIjR478bJ8QAAAAAFbSKsWysWPH5rjjjstf//rXPPjgg1myZEm+9rWv5Z133imtc/zxx+fPf/5zbr755owdOzb/+te/0rdv39LyZcuW5YADDsjixYszbty4XHfddRk5cmSGDh1aWmfGjBk54IADstdee+XZZ5/NkCFDMmjQoNx///1r4CMDAAAAwMertSor33fffdUejxw5MptsskkmTpyYPffcM/Pnz89vf/vbjBo1KnvvvXeSZMSIEencuXP++te/Ztddd80DDzyQKVOm5C9/+UtatGiR7bffPuecc05OOeWUDBs2LHXq1Mk111yT9u3b55JLLkmSdO7cOY8//nguu+yy9OnTZw19dAAAAACobrXuWTZ//vwkSbNmzZIkEydOzJIlS9K7d+/SOltttVU222yzjB8/Pkkyfvz4dOnSJS1atCit06dPnyxYsCAvvPBCaZ2PbuPDdT7cBgAAAAB8HlbpzLKPWr58eYYMGZLu3btnm222SZLMnj07derUSdOmTaut26JFi8yePbu0zkdD2YfLP1z2SessWLAg7733XurXr7/CPIsWLcqiRYtKjxcsWPBZPxoAAAAAG6jPfGbZcccdl+effz6jR49ek/N8Zueff36aNGlS+mnTpk25RwIAAABgHfOZYtngwYNz11135ZFHHsmmm25aer5ly5ZZvHhx5s2bV239119/PS1btiyt83+/HfPDx5+2TmVl5ceeVZYkp512WubPn1/6ee211z7LRwMAAABgA7ZKsayqqiqDBw/Obbfdlocffjjt27evtnynnXZK7dq189BDD5WemzZtWmbOnJlu3bolSbp165bJkydnzpw5pXUefPDBVFZWZuutty6t89FtfLjOh9v4OHXr1k1lZWW1HwAAAABYFat0z7Ljjjsuo0aNyh133JHGjRuX7jHWpEmT1K9fP02aNMnAgQNzwgknpFmzZqmsrMwPf/jDdOvWLbvuumuS5Gtf+1q23nrrfOc738lFF12U2bNn54wzzshxxx2XunXrJkmOPfbYXH311Tn55JNz9NFH5+GHH84f//jH3H333Wv44wMAAADA/7dKZ5b98pe/zPz589OzZ8+0atWq9HPTTTeV1rnsssvy9a9/PQcffHD23HPPtGzZMrfeemtpec2aNXPXXXelZs2a6datW/77v/87Rx55ZM4+++zSOu3bt8/dd9+dBx98MNttt10uueSS/OY3v0mfPn3WwEcGAAAAgI+3SmeWVVVVfeo69erVy89//vP8/Oc//4/rtG3bNvfcc88nbqdnz57529/+tirjAQAAAMBq+czfhgkAAAAA6xuxDAAAAAAKq3QZJgAAAABrp3bvjyr3CKvllXIPUHBmGQAAAAAUxDIAAAAAKIhlAAAAAFAQywAAAACgIJYBAAAAQEEsAwAAAICCWAYAAAAABbEMAAAAAApiGQAAAAAUxDIAAAAAKIhlAAAAAFAQywAAAACgIJYBAAAAQEEsAwAAAICCWAYAAAAABbEMAAAAAApiGQAAAAAUxDIAAAAAKIhlAAAAAFAQywAAAACgIJYBAAAAQEEsAwAAAICCWAYAAAAABbEMAAAAAApiGQAAAAAUxDIAAAAAKIhlAAAAAFAQywAAAACgIJYBAAAAQKFWuQdYl7V7f1S5R1htr5R7AAAAAIC1iDPLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAoiGUAAAAAUBDLAAAAAKAglgEAAABAQSwDAAAAgIJYBgAAAAAFsQwAAAAACmIZAAAAABTEMgAAAAAorNWx7Oc//3natWuXevXqZZdddsmTTz5Z7pEAAAAAWI+ttbHspptuygknnJCzzjorzzzzTLbbbrv06dMnc+bMKfdoAAAAAKyn1tpYdumll+aYY47JgAEDsvXWW+eaa65JgwYN8rvf/a7cowEAAACwnqpV7gE+zuLFizNx4sScdtpppedq1KiR3r17Z/z48R/7mkWLFmXRokWlx/Pnz0+SLFiw4HObc/midz+3bX9RPs/980VY14/Bur7/E8dgbeAYlJ9jUF7r+v5PHIO1gWNQfo5B+TkG5bWu7//EMVgbOAafvu2qqqpPXbeiamXW+oL961//ype//OWMGzcu3bp1Kz1/8sknZ+zYsZkwYcIKrxk2bFiGDx/+RY4JAAAAwDrktddey6abbvqJ66yVZ5Z9FqeddlpOOOGE0uPly5dn7ty52WijjVJRUVHGyT6bBQsWpE2bNnnttddSWVlZ7nE2SI5B+TkG5ecYlJ9jUH6OQfk5BuVl/5efY1B+jkH5OQblt64fg6qqqvz73/9O69atP3XdtTKWbbzxxqlZs2Zef/31as+//vrradmy5ce+pm7duqlbt26155o2bfp5jfiFqaysXCd/E65PHIPycwzKzzEoP8eg/ByD8nMMysv+Lz/HoPwcg/JzDMpvXT4GTZo0Wan11sob/NepUyc77bRTHnroodJzy5cvz0MPPVTtskwAAAAAWJPWyjPLkuSEE07IUUcdla5du+arX/1qLr/88rzzzjsZMGBAuUcDAAAAYD211sayQw89NG+88UaGDh2a2bNnZ/vtt899992XFi1alHu0L0TdunVz1llnrXBpKV8cx6D8HIPycwzKzzEoP8eg/ByD8rL/y88xKD/HoPwcg/LbkI7BWvltmAAAAABQDmvlPcsAAAAAoBzEMgAAAAAoiGUAAAAAUBDL1hL/+Mc/yj0CAAAAwAZPLFtLbLHFFtlrr73yhz/8Ie+//365xwGADdrf//733H///XnvvfeSJL4P6YvzyCOPlHsEksycOfNjf99XVVVl5syZZZhow7JkyZL06tUrL7/8crlH2WAtWbIkRx99dGbMmFHuUYAyEMvWEs8880y23XbbnHDCCWnZsmW+973v5cknnyz3WBskZ/kBbLjeeuut9O7dO506dcr++++fWbNmJUkGDhyYn/zkJ2WebsOw7777ZvPNN8+5556b1157rdzjbLDat2+fN954Y4Xn586dm/bt25dhog1L7dq1M2nSpHKPsUGrXbt2/vSnP5V7jA3asmXL8uijj2bevHnlHoUNUEWVfypdqyxdujR33nlnRo4cmfvuuy+dOnXK0Ucfne985ztp3rx5ucfbINSoUSM9evTIwIED861vfSv16tUr90gbnHnz5uXJJ5/MnDlzsnz58mrLjjzyyDJNtf464YQTVnrdSy+99HOchA898sgj2Wuvvco9xgbpyCOPzJw5c/Kb3/wmnTt3znPPPZcOHTrk/vvvzwknnJAXXnih3COu9958881cf/31ue666/LCCy9k7733zsCBA3PQQQelTp065R5vg1GjRo28/vrrK/z356uvvpqtt94677zzTpkm23Acf/zxqVu3bi644IJyj7LBOuqoo7L99tvn+OOPL/coG6x69epl6tSpIn2ZLFmyJPXr18+zzz6bbbbZptzjfKHEsrXUokWL8otf/CKnnXZaFi9enDp16uSQQw7JhRdemFatWpV7vPXas88+mxEjRuTGG2/M4sWLc+ihh2bgwIH56le/Wu7RNgh//vOf069fvyxcuDCVlZWpqKgoLauoqMjcuXPLON36aWWjTEVFRR5++OHPeRqSpG7dutl0000zYMCAHHXUUWnTpk25R9pgtGzZMvfff3+22267NG7cuBTL/vGPf2TbbbfNwoULyz3iBuWZZ54p/X9ykhxxxBEZOHBgtttuuzJPtv768B9QrrjiihxzzDFp0KBBadmyZcsyYcKE1KxZM0888US5Rtxg/PCHP8zvf//7dOzYMTvttFMaNmxYbbl/wPr8nXvuubnkkkvSq1evjz0GP/rRj8o02Yaja9euufDCC9OrV69yj7LB6tChQ2677bYN7v97xbK1zNNPP53f/e53GT16dBo2bJijjjoqAwcOzD//+c8MHz48CxYscHnmF8RZfuXx4aVP5513XrX/QIcNiTNryqdx48Z55pln0rFjx2qx7Omnn06fPn3y1ltvlXvEDc6//vWv/PrXv84FF1yQWrVq5f3330+3bt1yzTXX5Ctf+Uq5x1vvfPgPKGPHjk23bt2q/ZlTp06dtGvXLieeeGI6duxYrhE3GJ/0j1n+AeuL8UlnM1VUVLh9yxfgvvvuy2mnnZZzzjnnY4NlZWVlmSbbcPz2t7/Nrbfemuuvvz7NmjUr9zhfGLFsLXHppZdmxIgRmTZtWvbff/8MGjQo+++/f2rU+P+3lfvnP/+Zdu3aZenSpWWcdMPjLL8vVsOGDTN58uR06NCh3KPAWsGZNV+s/fffPzvttFPOOeecNG7cOJMmTUrbtm1z2GGHZfny5bnlllvKPeIGYcmSJbnjjjvyu9/9Lg8++GC6du2agQMH5vDDD88bb7yRM844I88880ymTJlS7lHXWwMGDMgVV1zhL6JAWX3078MfveKkqqoqFRUVWbZsWTnG2qDssMMO+fvf/54lS5akbdu2KwTLZ555pkyTfb7EsrVEx44dc/TRR6d///7/McAsXrw4N954Y4466qgveLoNk7P8yqNv37457LDDcsghh5R7lA1G3759M3LkyFRWVqZv376fuO6tt976BU3FRzmz5ovz/PPPp1evXtlxxx3z8MMP57/+67/ywgsvZO7cuXniiSey+eabl3vE9d4Pf/jD3Hjjjamqqsp3vvOdDBo0aIX7pMyePTutW7de4b6WfH4WLFiQhx9+OFtttVW22mqrco+zQfn73/+e6dOnZ88990z9+vVLkYAvzuLFizNjxoxsvvnmqVWrVrnH2aCMHTv2E5f36NHjC5pkwzV8+PBPXH7WWWd9QZN8scSytcDSpUtz7rnnZtCgQdl0003LPc4Gz1l+X7w777yz9Os33ngjZ599dgYMGJAuXbqkdu3a1db9r//6ry96vPXegAEDcuWVV6Zx48YZMGDAJ647YsSIL2gqnFlTPvPnz8/VV1+d5557LgsXLsyOO+6Y4447ztnEX5BevXpl0KBB6du3b+rWrfux6yxdujRPPPGEvyR9jg455JDsueeeGTx4cN57771st912eeWVV1JVVZXRo0fn4IMPLveI67233norhxxySB555JFUVFTk5ZdfTocOHXL00UfnS1/6Ui655JJyj7jee/fdd/PDH/4w1113XZLkpZdeSocOHfLDH/4wX/7yl3PqqaeWeULg8yKWrSUaN26cyZMn/7/27j6u5vv/H/jjVEoqiZRl6WKloUyuJhdznbAstrkoFMbClCH0GaIRM5vMTC5K9FlqiI1RH2PrQm2JVK6SSEbNRYWKqM7vD5zPjtrY9+ecV5/zftxvt2633q/3u3MeRL3P87xezxesra1FR5E8zvJTvz8XIv8Op1qrllwux9WrV9GyZUvo6+uLjiNpnFlDRKL9ebOL6OhoBAUFISsrC9u3b8fmzZuRmZkpOqLG4+684vn7++PYsWMIDQ2Fm5sbsrOzYWtri++//x5Lly7l/wM1SU5OxqZNm3Dp0iXs2rULrVu3RlRUFGxsbNC7d2/R8SShrKwMu3fvRn5+PgICAtC8eXOcPHkS5ubmaN26teh4KsE5pA3EgAEDkJiYyGKZYNXV1fDy8sKECRP+dgaBrq4uC2UvEV/sNwxyuRx2dnY4c+YMGzcLdvbsWaxfv/5vZ9aYmpri559/VnMyaSgrK0N6ejpu3LhR5+fTxIkTBaXSbH+eYfw8nGGsHnfu3FE0co6Pj8e7776LJk2aYPjw4QgICBCcThr+85//ICEhoc7KE3t7e1y5ckVQKmnZt28fYmNj0aNHD6Wlrx06dEB+fr7AZNKxZ88eTJgwAV5eXjh58iSqqqoAPP4ZFRISgoMHDwpOqPmys7MxaNAgGBsbo6CgAFOnTkXz5s0RFxeHwsJC7NixQ3RElWCxrIEYOnQoFi5ciJycnHp3+eCNoXro6Ojgiy++gI+Pj+goRGqnpaUFe3t73L59m8UywY4cOfLca3R0dLgETQX2798PLy8vlJeXo2nTpkovjmQyGYtlKuLh4fFC13GGsfpYWloiLS0NzZs3R3x8PGJiYgAApaWlaNy4seB00lBRUVHvzuAlJSV/+UYKvVw3b96EmZlZnfGKigr2jVOT5cuXIywsDBMnTlT8HAKAXr16Yfny5QKTScecOXPg4+OD1atXw8jISDE+bNgweHp6CkymWiyWNRAzZswA8Lhf1rN4Y6henOUnlp+fH+zs7ODn56c0/vXXX+PixYsIDQ0VE0wiVq1ahYCAAGzcuLHOsj9Sv7Nnz6KwsBAPHz5UGucbKKozd+5cTJ48GSEhIfW+SCXV4Azjhmf27Nnw8vKCoaEhrKys0K9fPwBAUlISnJycxIaTiD59+mDHjh349NNPATx+TVBbW4vVq1ejf//+gtNJQ9euXfHjjz9i1qxZAP67G+PWrVvh4uIiMppk5Obm4q233qozbmxsjLKyMvUHkqDjx49j06ZNdcZbt26N4uJiAYnUg8WyBoI3iQ0HZ/mJtWfPnnqX4/Ts2ROrVq1isUzFJk6ciMrKSrzxxhvQ1dWt07uspKREUDJpuXTpEkaOHImcnBzIZDI8bS/69Cadb6CozrVr1+Dn58dCGUnejBkz0L17d1y9ehWDBw9W9Be1tbXlbA41Wb16NQYOHIiMjAw8fPgQ8+fPV9qdl1QvJCQEQ4cOxdmzZ1FdXY1169bh7NmzSE1Nfe4ujfRytGrVChcvXqwzkSElJQW2trZiQkmMnp4e7t69W2f8woULaNmypYBE6sEG/0TP+Ltm85zlp3qNGzfG6dOnYWdnpzR+8eJFODo64sGDB4KSScPT3Z7+Cnv1qYe7uzu0tbWxdetW2NjYID09Hbdv38bcuXOxZs0a9OnTR3REjTVq1CiMHTsWo0ePFh1FsoKDg//2/JIlS9SUhEg87s4rXn5+PlatWqX0PViwYAFnWKrJypUr8e9//xsREREYPHgwDh48iCtXruDjjz/G4sWLFbP+SHU++OAD3L59G9999x2aN2+O7OxsaGtrw8PDA2+99ZbGTmZgsawBqaioQGJiYr1Lbp5dkkakqRwdHeHr64uPPvpIaXz9+vXYuHEjzp49KygZkfqYmpri6NGj6NixI4yNjZGeng4HBwccPXoUc+fO5e5bKhQeHo7g4GBMmjQJTk5OaNSokdJ5zi5WPWdnZ6XjR48e4fLly9DR0cFrr72GkydPCkomLZMnT/7b8xEREWpKQkRSJpfLERISgpUrV6KyshLA45lO8+bNUyxRJtW6c+cO3nvvPWRkZODevXuwsLBAcXExXFxccPDgwTorsTQFi2UNRGZmJoYNG4bKykpUVFSgefPmuHXrFpo0aQIzMzNcunRJdEQitYiIiMBHH32EgIAADBgwAMDjZudffPEFQkNDMXXqVMEJNV9NTQ327t2Lc+fOAQDat2+Pd955Bzo6XLmvLiYmJjh58iRsbGzw2muvYevWrejfvz/y8/Ph5OSkuFmkl4+zixumu3fvwsfHByNHjsSECRNEx5GEkSNHKh0/evQIp0+fRllZGQYMGIC4uDhByaSltLQU4eHhSr+TJ02apNiplFSP90UNw8OHD3Hx4kWUl5ejffv2MDQ0FB1JclJSUpCdna2YYTlo0CDRkVSKxbIGol+/fmjbti3CwsJgbGyMrKwsNGrUCOPHj4e/vz9GjRolOqKkcJafWBs3bsSKFStw/fp1AIC1tTWWLl3KXejU4MyZMxgxYgSKi4vh4OAA4L/9CPbv38+m/2rSp08fzJ07Fx4eHvD09ERpaSkWLVqEzZs348SJEzh9+rToiERql5OTA3d3dxQUFIiOIlm1tbWYPn06XnvtNcyfP190HI2XlJQEd3d3GBsbo2vXrgCAEydOoKysDPv376+36Tm9XLwvaliuXr0K4PFuvUSqxmJZA9GsWTP89ttvcHBwQLNmzZCWloZ27drht99+g7e3N86fPy86omRwlp841dXViI6OxpAhQ2Bubo6bN29CX1+f7xypkYuLC1q2bInt27fDxMQEwON3tX18fHDz5k2kpqYKTigNCQkJqKiowKhRo5CXlwd3d3dcuHABLVq0QExMDAYOHCg6IpHapaSkwN3dHaWlpaKjSFpubi769euHoqIi0VE0npOTE1xcXLBx40Zoa2sDeDzLacaMGUhNTUVOTo7ghJqP90XiVVdXY9myZfjqq69QXl4OADA0NMSsWbMQFBRUp10CqcaRI0ewdu1axQzLdu3aYfbs2Ro9u4zFsgaiZcuWSE1Nhb29Pdq2bYv169djyJAhOH/+PLp06YKKigrRESWDs/zEatKkCc6dOwcrKyvRUSRJX18fGRkZ6NChg9L46dOn0a1bN9y/f19QMiopKYGJiYliR0xSDTaXF++rr75SOpbL5SgqKkJUVBT69u2L6OhoQckIAA4ePAhvb2/cvHlTdBSNp6+vj1OnTilmND2Vm5uLTp068XeyGvC+SLzp06cjLi4OwcHBcHFxAQCkpaVh6dKl8PDwwMaNGwUn1HzffPMN/P398d577ym+B7/++it2796NtWvXYubMmYITqgYXWjcQzs7OOH78OOzt7dG3b18sWbIEt27dQlRUFKf3qtmpU6ewadMmaGlpQVtbG1VVVbC1tcXq1avh7e3NYpmKde/eHZmZmSyWCdK2bVv88ccfdW4Kb9y4UWeHUnr5ntdQ+yk21ladvXv3Kh0/21yexTLVW7t2rdKxlpYWWrZsCW9vbwQGBgpKJT1z5sxROn5atPzxxx+5M7KadO7cGefOnatTLDt37hzeeOMNQamkhfdF4kVHRyMmJgZDhw5VjHXs2BGWlpYYN24ci2VqEBISgrVr1yptwObn54devXohJCSExTJSrZCQENy7dw8AsGLFCkycOBHTp0+Hvb09XxSpWaNGjRQNns3MzFBYWIh27drB2NhYsU6eVGfGjBmYO3cufv/9d3Tp0qXO7iodO3YUlEwaVq5cCT8/PyxduhQ9evQA8Pido+DgYHz22We4e/eu4tqmTZuKiqmxIiMjYWVlBWdnZ3Ditxj17TT65+bypHqXL18WHYFQ9//C06LlF1988cKFffrnsrOzFZ/7+fnB398fFy9eVPqdvGHDBqxatUpURI3353ud590Xkerp6enB2tq6zriNjQ10dXXVH0iCysrK4ObmVmfc1dUVCxYsEJBIPbgMk+gZrq6u8PHxgaenJ6ZOnYrs7Gz4+fkhKioKpaWl+O2330RH1Gj17UQnk8kgl8u5E50a/Pnv/+lyv6e/Jv58zO+FasycORM7d+6ElZUVJk2ahPHjx3PHswaCzeVV70Vmbuvo6KBVq1YYPHgw3N3d1ZCKSL20tLQU9z1/h7+HVefp9+Cp+u6Dnh7ze6B6wcHBOH/+PLZt2wY9PT0AQFVVFaZMmQJ7e3sEBQUJTqj5PD094ezsjICAAKXxNWvWICMjAzExMYKSqRZnlhE9g7P8xOKMArF+/vnnvzyXnZ3NmX0qtmHDBnz55ZeIi4tDREQEAgMDMXz4cEyZMgWurq7sVybQnTt3cOfOHdExNJqxsfFzr6mtrUVeXh62bt2KefPmPbfHHNH/Gt4Hifd390KkHs++efLTTz/h1VdfVSw/zsrKwsOHD7nhkQr9uX9o+/btsWLFCvzyyy9KPcuOHTuGuXPnioqocpxZ1kDY2Nj87Ysg7sBIRCLcu3cPO3fuxNatW3HixAm+g6pmV65cQWRkJHbs2IHq6mqcOXOGu8OqGJvL/284cOAAZsyYgcLCQtFRNJazs3O996YymQyNGzeGnZ0dfHx80L9/fwHpiEiTTZo06YWv3bZtmwqTSJeNjc0LXSeTyTS2VsGZZQ3E7NmzlY4fPXqEzMxMxMfH15nuSCQFZ8+eRWFhIR4+fKg0PmLECEGJpCUpKQnh4eHYs2cPLCwsMGrUKGzYsEF0LMn583IcFirVg83l/zf07t0bXbt2FR1Do7m5uWHjxo1wcnJC9+7dAQDHjx9HdnY2fHx8cPbsWQwaNAhxcXF45513BKfVXNevX0dKSgpu3LiB2tpapXN+fn6CUklLWVkZwsPDce7cOQBAhw4dMHny5BeaDUv/NyyAicdZrpxZ1uBt2LABGRkZ/IGhRpzlJ9alS5cwcuRI5OTkKPXsePo9YcFAdYqLixEZGYnw8HDcvXsXo0ePRlhYGLKystC+fXvR8SSjqqpKsQwzJSUFb7/9NiZNmgQ3N7d6e/oREanC1KlT0aZNGyxevFhpfPny5bhy5Qq2bNmCoKAg/Pjjj8jIyBCUUrNFRkbiww8/hK6uLlq0aKF0f6rJszkakoyMDAwZMgT6+vpKReP79+/jP//5Dzp37iw4IRGpCotlDdylS5fQqVMnpV1ZSLXWrVundPzsLL+FCxcKSiYN7u7u0NbWxtatW2FjY4P09HTcvn0bc+fOxZo1a9CnTx/RETWSu7s7kpKSMHz4cHh5ecHNzQ3a2tpo1KgRi2VqNGPGDMTExMDS0hKTJ0+Gl5cXTE1NRceSBDaXJ1JmbGyMEydOwM7OTmn84sWL6NKlC+7cuYPz58+jW7duil6v9HJZWlrC19cXgYGBfLNEkD59+sDOzg5btmyBjs7jRVnV1dX44IMPcOnSJSQlJQlOqPlu376NJUuW4Oeff653hmVJSYmgZNIhl8uxe/fuv/wexMXFCUqmWlyG2cDt3r2bO6Gpmb+/f73jT2f5kWqlpaXh6NGjMDU1hZaWFrS0tNC7d2/F1t3PbmVPL8ehQ4fg5+en2MyCxAgLC0ObNm1ga2uLxMREJCYm1nudpt6UiMTm8kTKGjdujNTU1DrFstTUVDRu3BjA4/8TTz+nl6+yshJjx45loUygjIwMpUIZ8PiNk/nz53MpuJpMmDABFy9exJQpU2Bubs7NjgSYPXs2Nm3ahP79+0vqe8BiWQPxbBNVuVyO4uJi3Lx5E998843AZPTU0KFDERgYyCWxKlZTUwMjIyMAgKmpKa5fvw4HBwdYWVkhNzdXcDrNlZKSgvDwcHTp0gXt2rXDhAkTMHbsWNGxJGfixImSuQFpaP7Jz/anzeVZLCNNNmvWLPj6+uLEiRPo1q0bgMfLz7Zu3Yp//etfAICEhAR06tRJYErNNmXKFOzatYurGgRq2rQpCgsL8frrryuNX716VXG/SqqVnJyMlJQUxU6YpH5RUVGIi4vDsGHDREdRKxbLGggPDw+l46cNhfv161fnhzOJwVl+6uHo6IisrCzY2NjgzTffxOrVq6Grq4vNmzfD1tZWdDyN1aNHD/To0QOhoaGIjY1FREQE5syZg9raWhw+fBiWlpa8KVSDyMhI0RHoBbC5PEnBokWLYGNjg6+//hpRUVEAAAcHB2zZsgWenp4AAF9fX0yfPl1kTI22cuVKvP3224iPj4eTkxMaNWqkdP7LL78UlEw6xowZgylTpmDNmjXo2bMnAODYsWMICAjAuHHjBKeThtdffx33798XHUPSjI2NJfk6jD3LiJ7xvFl+06ZNE5hO8yUkJKCiogKjRo1CXl4e3N3dceHCBbRo0QIxMTEYOHCg6IiSkZubi/DwcERFRaGsrAyDBw/GDz/8IDoWERGRJCxfvhxLliyBg4NDnaVPMpkMR48eFZhOGh4+fIiAgACEhYWhuroaANCoUSNMnz4dq1atgp6enuCEmu/48eNYuHAhlixZAkdHxzpF46ZNmwpKJh3bt29HfHw8IiIioK+vLzqO2rBY1oDU1tbi4sWL9TbNe+uttwSlkp5ly5YpHXOWn3glJSUwMTHh8jRBampqsH//fkRERLBYRkREpCYmJiZYu3YtfHx8REeRvMrKSuTn5wMAXnvtNTRp0kRwIunIy8uDp6cnTp48qTQul8shk8lQU1MjKJl03L9/HyNHjsSxY8dgbW1dp2D57PdGU3AZZgPx66+/wtPTE1euXMGz9Uv+EFCf6upq2NjYYMiQITA3NxcdR1ImT578QtdFRESoOAk9S1tbGx4eHnWWixMRkebS0tL62zepeG+qenp6eujVq5foGJI2efJkrFu3DkZGRnByclKMV1RUYNasWbwvVQMvLy80atQI0dHRkmou35B4e3vjxIkTGD9+vKS+B5xZ1kB06tQJbdu2xbJly/DKK6/U+Qf4Irt00cvRpEkTnDt3DlZWVqKjSIqWlhasrKzg7Oxcp2D8Z3v37lVjKiIiImn6/vvvlY4fPXqEzMxMbN++HcuWLcOUKVMEJZOOlStXoqioCF999ZXoKJKlra2NoqIimJmZKY3funULrVq1UizNJNVp0qQJMjMz4eDgIDqKZBkYGCAhIQG9e/cWHUWtOLOsgcjLy8Pu3bvrbM9N6te9e3dkZmayWKZm06dPx86dO3H58mVMmjQJ48eP54YKREREgrzzzjt1xt577z106NABsbGxLJapQXp6Oo4ePYoDBw6gQ4cOdZY+xcXFCUqm+e7evQu5XA65XI579+6hcePGinM1NTU4ePBgnQIaqUbXrl1x9epVFssEsrS0lGRvOM4sayAGDBiA+fPnw83NTXQUyfvuu+8QGBiIjz/+GF26dIGBgYHS+Y4dOwpKpvmqqqoQFxeHiIgIpKamYvjw4ZgyZQpcXV0lM92XiIioIbt06RI6duyI8vJy0VE03qRJk/72/LZt29SURHqetwxZJpNh2bJl+OSTT9SYSpp27dqFpUuXIiAgoN5dYfnaTPV+/PFHrF+/HmFhYbC2thYdR21YLGsg9u7di0WLFvGHQAOgpaVVZ0wmk7GJpJpduXIFkZGR2LFjB6qrq3HmzBkYGhqKjkVERCRZ9+/fR2BgIA4dOoTc3FzRcYhUJjExEXK5HAMGDMCePXuUVjvo6urCysoKFhYWAhNKB1+biWdiYoLKykpUV1ejSZMmdWoVJSUlgpKpFpdhNhDvvvsuAOUm5/whIMbly5dFRyD89x09uVzOf/9ERERq9uwu1E+Xo+nr6+Pbb78VmExaqqur8csvvyA/Px+enp4wMjLC9evX0bRpU76JqEJ9+/YF8Ph1QZs2bbjCQSC+NhMvNDRUdAQhOLOsgbhy5crfnmf/LJKCPy/DTElJwdtvv41JkybBzc2t3neViIiISDW2b9+udKylpYWWLVvizTffxLVr1+Do6CgomXRcuXIFbm5uKCwsRFVVFS5cuABbW1v4+/ujqqoKYWFhoiNqvG3btsHQ0BDvv/++0viuXbtQWVkJb29vQcmISNVYLCOqR25uLtavX49z584BANq1a4dZs2axsaQKzZgxAzExMbC0tMTkyZPh5eUFU1NT0bGIiIgIwL1797Bz506Eh4cjIyODs77VwMPDA0ZGRggPD0eLFi2QlZUFW1tb/PLLL5g6dSry8vJER9R4bdu2xaZNm9C/f3+l8cTEREybNo3LkdUkKioKYWFhuHz5MtLS0mBlZYXQ0FDY2NjUuxkJvXz5+fnYtm0b8vPzsW7dOpiZmeHQoUNo06YNOnToIDqeSnAZZgNz9uxZFBYW4uHDh0rjI0aMEJRIevbs2YOxY8eia9eucHFxAQD8+uuvcHR0RExMjGLJLL1cYWFhaNOmDWxtbZGYmIjExMR6r+POT0REROqTlJSE8PBw7NmzBxYWFhg1ahS+/vpr0bEkITk5GampqdDV1VUat7a2xrVr1wSlkpbCwkLY2NjUGbeyskJhYaGARNKzceNGLFmyBLNnz8aKFSsUhfpmzZohNDSUxTI1SExMxNChQ9GrVy8kJSVhxYoVMDMzQ1ZWFsLDw7F7927REVWCxbIG4tKlSxg5ciRycnIUfZoAKNbH89079Zk/fz4CAwMRHBysNB4UFIT58+ezWKYiEydOZD8IIiKiBqC4uBiRkZEIDw/H3bt3MXr0aFRVVWHfvn1o37696HiSUVtbW+9rgN9//x1GRkYCEkmPmZkZsrOz6+wAmJWVhRYtWogJJTHr16/Hli1b4OHhgVWrVinGu3btinnz5glMJh0LFy7E8uXLMWfOHKWfPQMGDNDoN09YLGsg/P39YWNjgyNHjsDGxgbp6em4ffs25s6dizVr1oiOJylFRUWYOHFinfHx48fj888/F5BIGiIjI0VHICIikjx3d3ckJSVh+PDhCA0NhZubG7S1tdkfSwBXV1eEhoZi8+bNAB6/iV5eXo6goCAMGzZMcDppGDduHPz8/GBkZIS33noLwONZNv7+/hg7dqzgdNJw+fJlODs71xnX09NDRUWFgETSk5OTg+jo6DrjZmZmuHXrloBE6sFiWQORlpaGo0ePwtTUFFpaWtDS0kLv3r2xcuVK+Pn5ITMzU3REyejXrx+Sk5NhZ2enNJ6SkoI+ffoISkVERESkeocOHYKfnx+mT58Oe3t70XEk7YsvvsCQIUPQvn17PHjwAJ6ensjLy4OpqSl27twpOp4kfPrppygoKMDAgQOho/P4pXNNTQ28vb0REhIiOJ002NjY4NSpU3U2vIuPj0e7du0EpZKWZs2aoaioqM6S5MzMTLRu3VpQKtVjsayBqKmpUUxpNDU1xfXr1+Hg4AArKys2jlSDH374QfH5iBEjsGDBApw4cQI9evQA8Lhn2a5du7Bs2TJREYmIiIhULiUlBeHh4ejSpQvatWuHCRMmcAaNIK+++iqysrIQGxuLrKwslJeXY8qUKfDy8oK+vr7oeJKgq6uL2NhYzJs3DwUFBdDX14eTk1Odwg2pzpw5czBz5kw8ePAAcrkc6enp2LlzJ1auXImtW7eKjicJY8eOxYIFC7Br1y7IZDLU1tbi2LFjmDdvXr0rsjQFd8NsIPr06YO5c+fCw8MDnp6eKC0txaJFi7B582acOHECp0+fFh1Ro2lpab3QdTKZjP3jiIiISONVVFQgNjYWERERSE9PR01NDb788ktMnjyZ/bLUJCkpCT179lTMaHqquroaqampimWBpBplZWX45JNPEBsbi9LSUgCAiYkJxo4di+XLl6NZs2ZiA0rIt99+i6VLlyI/Px8AYGFhgWXLlmHKlCmCk0nDw4cPMXPmTERGRqKmpgY6OjqoqamBp6cnIiMjoa2tLTqiSrBY1kAkJCSgoqICo0aNQl5eHtzd3XHhwgW0aNECMTExGDhwoOiIRERERCRBubm5CA8PR1RUFMrKyjB48GClWfmkGtra2igqKoKZmZnS+O3bt2FmZsY3cFWopKQELi4uuHbtGry8vBTL/c6ePYvo6GhYWloiNTUVJiYmgpNKS2VlJcrLy+v8nyD1uHr1KnJyclBeXg5nZ2eNX6rPYlkDVlJSAhMTE+4QKNCDBw/QuHFj0TGIiIiIhKupqcH+/fsRERHBYpkaaGlp4Y8//kDLli2Vxi9cuICuXbvi7t27gpJpvtmzZ+PIkSP46aefYG5urnSuuLgYrq6uGDhwINauXSsooXQsX74cXl5edfplEakai2WCjRo16rnX6OjooFWrVhg8eDDc3d3VkEraampqEBISgrCwMPzxxx+4cOECbG1tsXjxYlhbW3O6LxERERGpzNPXB99//z3c3Nygp6enOFdTU4Ps7Gw4ODggPj5eVESNZ21tjU2bNmHIkCH1no+Pj4evry8KCgrUG0yC3njjDZw+fRpvvvkmxo8fj9GjR8PU1FR0LEl599130b17dyxYsEBpfPXq1Th+/Dh27dolKJlqvVijJlIZY2Pj537o6+sjLy8PY8aMwZIlS0RH1ngrVqxAZGQkVq9eDV1dXcW4o6Mjm0gSERERkUo9fQ0gl8thZGSk9LqgVatWmDZtGv7973+LjqnRioqK0KFDh7887+joiOLiYjUmkq6srCxkZ2ejX79+WLNmDSwsLDB8+HBER0ejsrJSdDxJSEpKwrBhw+qMDx06FElJSQISqQdnlv0POXDgAGbMmIHCwkLRUTSanZ0dNm3ahIEDB8LIyAhZWVmwtbXF+fPn4eLiomjwSURERESkKsuWLcO8efNgYGAgOorktG7dGrGxsejdu3e955OTkzFmzBhcv35dzcno2LFjiI6Oxq5du/DgwQMuR1YDfX19nDp1Cg4ODkrj58+fh7OzM+7fvy8omWpxZtn/kN69e6Nr166iY2i8a9euwc7Ors54bW0tHj16JCAREREREUlNUFAQDAwMcOPGDSQnJyM5ORk3btwQHUsShgwZgk8++QQPHz6sc66qqgqLFy+Gm5ubgGRkYGAAfX196Orq8rWZmjg5OSE2NrbOeExMDNq3by8gkXroPP8SaiiaNWuGuLg40TE0Xvv27ZGcnAwrKyul8d27d8PZ2VlQKiIiIiKSknv37mHGjBmIiYlR7Hypra2NMWPGYMOGDTA2NhacUHMFBweja9eusLe3x8yZM/H6669DLpfj3Llz+Oabb1BVVYWoqCjRMSXj8uXLiI6ORnR0NHJzc9G3b18sW7YM7733nuhokrB48WKMGjUK+fn5GDBgAADgyJEj2Llzp8b2KwNYLCOqY8mSJfD29sa1a9dQW1uLuLg45ObmYseOHThw4IDoeEREREQkAR988AEyMzNx4MABuLi4AADS0tLg7++PDz/8EDExMYITaq5XX30VaWlpmDFjBgIDA/G0c5FMJsPgwYPx9ddfw9LSUnBKaejRoweOHz+Ojh07YtKkSRg3bhxat24tOpakuLu7Y9++fQgJCcHu3buhr6+Pjh074qeffkLfvn1Fx1MZ9iwjeuLSpUuwsbGBTCZDcnIygoODkZWVhfLycnTu3BlLliyBq6ur6JhEREREJAEGBgZISEio0zcrOTkZbm5uqKioEJRMWkpLS5GXlwfgcW/j5s2bC04kLZ988gm8vLw0erkfNUycWUb0hL29PYqKimBmZoY+ffqgefPmyMnJgbm5uehoRERERCQxLVq0qHeppbGxMUxMTAQkkiYTExN0795ddAzJWrFiheLzP8/wI/UqKyvD7t27cenSJcybNw/NmzfHyZMnYW5urrEz/djgn+iJZydZHjp0iO/YEREREZEQixYtwpw5c1BcXKwYKy4uRkBAABYvXiwwGZF67dixA05OTtDX11csAWTPOPXJzs5G27Zt8dlnn+Hzzz9HWVkZACAuLg6BgYFiw6kQZ5YR/QWuUCYiIiIiUTZu3IiLFy+iTZs2aNOmDQCgsLAQenp6uHnzJjZt2qS49uTJk6JiEqnUl19+icWLF+Ojjz5Cr169AAApKSnw9fXFrVu38PHHHwtOqPnmzJkDHx8frF69GkZGRorxYcOGwdPTU2Ay1WKxjOgJmUxWZ0ovp/gSERERkQgeHh6iIxAJt379emzcuBETJ05UjI0YMQIdOnTA0qVLWSxTg+PHjysV559q3bq10sxXTcNiGdETcrkcPj4+0NPTAwA8ePAAvr6+MDAwULouLi5ORDwiIiIikpCgoCDREYiEKyoqQs+ePeuM9+zZE0VFRQISSY+enh7u3r1bZ/zChQto2bKlgETqwZ5lRE94e3vDzMwMxsbGMDY2xvjx42FhYaE4fvpBRERERKQOZWVl2Lp1KwIDA1FSUgLg8ZLLa9euCU5GpB52dnb47rvv6ozHxsbC3t5eQCLpGTFiBIKDg/Ho0SMAj1dfFRYWYsGCBXj33XcFp1MdmZyNmYiIiIiIiBqU7OxsDBo0CMbGxigoKEBubi5sbW2xaNEiFBYWYseOHaIjEqncnj17MGbMGAwaNEjRs+zYsWM4cuQIvvvuO4wcOVJwQs13584dvPfeezh+/DjKy8thYWGB4uJiuLi44ODBg3VWYmkKFsuIiIiIiIgamEGDBqFz586KptpZWVmwtbVFamoqPD09UVBQIDoikVqcOHECX375Jc6fPw8AaNeuHebOnQtnZ2fByaTl2LFjyMrKQnl5OTp37oxBgwaJjqRS7FlGRERERETUwEi1qTbRs7p06YJvv/1WdAxJqq2tRWRkJOLi4lBQUACZTAYbGxu0atUKcrlcozfEY88yIiIiIiKiBkaqTbWJAEBLSwva2tp/+6Gjw7k/qiSXyzFixAh88MEHuHbtGpycnNChQwdcuXIFPj4+Gr8Elv+6iIiIiIiIGpinTbWfNjeXSlNtIgDYu3fvX55LS0vDV199hdraWjUmkp7IyEgkJSXhyJEj6N+/v9K5o0ePwsPDAzt27MDEiRMFJVQt9iwjIiIiIiJqYJ421c7IyMC9e/dgYWGBoqIiuLi44NChQxrbVJvor+Tm5mLhwoXYv38/vLy8EBwcDCsrK9GxNJarqysGDBiAhQsX1ns+JCQEiYmJSEhIUHMy9WCxjIiIiIiIqIFKSUlBdnY2ysvL0aVLFwwcOFB0JCK1un79OoKCgrB9+3YMGTIEK1euhKOjo+hYGq9Vq1aIj49Hp06d6j2fmZmJoUOHamwPRfYsIyIiIiIiaiDS0tJw4MABxXHv3r1hYGCAb775BuPGjcO0adNQVVUlMCGRety5cwcLFiyAnZ0dzpw5gyNHjmD//v0slKlJSUkJzM3N//K8ubk5SktL1ZhIvVgsIyIiIiIiaiCCg4Nx5swZxXFOTg6mTp2KwYMHK5agrVy5UmBCItVbvXo1bG1tceDAAezcuROpqano06eP6FiSUlNT87ebKGhra6O6ulqNidSLyzCJiIiIiIgaiFdeeQX79+9H165dAQCffPIJEhMTkZKSAgDYtWsXgoKCcPbsWZExiVRKS0sL+vr6GDRoELS1tf/yuri4ODWmkhYtLS0MHToUenp69Z6vqqpCfHw8ampq1JxMPbgbJhERERERUQNRWlqqtPQpMTERQ4cOVRx369YNV69eFRGNSG0mTpwImUwmOoakeXt7P/caTd0JE2CxjIiIiIiIqMEwNzfH5cuXYWlpiYcPH+LkyZNYtmyZ4vy9e/fQqFEjgQmJVC8yMlJ0BMnbtm2b6AhCsWcZERERERFRAzFs2DAsXLgQycnJCAwMRJMmTZR6NWVnZ+O1114TmJCISPNxZhkREREREVED8emnn2LUqFHo27cvDA0NsX37dujq6irOR0REwNXVVWBCIiLNxwb/REREREREDcydO3dgaGhYp7l5SUkJDA0NlQpoRET0crFYRkRERERERERE9AR7lhERERERERERET3BYhkREREREREREdETLJYRERERERERERE9wWIZERERERERERHREyyWEREREb0Aa2trhIaGvrTHKygogEwmw6lTp/5PX//LL79AJpOhrKzspWXSBEuXLkWnTp1ExyAiIqL/YSyWEREREb2A48ePY9q0aaJj/H/p168fZs+eLTrGSyOTybBv3z7RMYiIiEjDsFhGRERE9MSjR4/qjD18+BAA0LJlSzRp0kTdkYiIiIhIzVgsIyIiIo0VHx+P3r17o1mzZmjRogXefvtt5OfnA/jvMsjY2Fj07dsXjRs3xrfffgsfHx94eHhgxYoVsLCwgIODAwDlZZienp4YM2aM0nM9evQIpqam2LFjx3Of+//i4MGDaNu2LfT19dG/f38UFBQonb99+zbGjRuH1q1bo0mTJnBycsLOnTsV5318fJCYmIh169ZBJpNBJpMpHuP06dMYOnQoDA0NYW5ujgkTJuDWrVsvlKtfv36YNWsWZs+eDRMTE5ibm2PLli2oqKjApEmTYGRkBDs7Oxw6dEjp6xITE9G9e3fo6enhlVdewcKFC1FdXa30uH5+fpg/fz6aN2+OVq1aYenSpYrz1tbWAICRI0dCJpMpjp+KioqCtbU1jI2NMXbsWNy7d++F/jxERERELJYRERGRxqqoqMCcOXOQkZGBI0eOQEtLCyNHjkRtba3imoULF8Lf3x/nzp3DkCFDAABHjhxBbm4uDh8+jAMHDtR5XC8vL+zfvx/l5eWKsYSEBFRWVmLkyJEv/Nwv6urVqxg1ahTc3d1x6tQpfPDBB1i4cKHSNQ8ePECXLl3w448/4vTp05g2bRomTJiA9PR0AMC6devg4uKCqVOnoqioCEVFRbC0tERZWRkGDBgAZ2dnZGRkID4+Hn/88QdGjx79wvm2b98OU1NTpKenY9asWZg+fTref/999OzZEydPnoSrqysmTJiAyspKAMC1a9cwbNgwdOvWDVlZWdi4cSPCw8OxfPnyOo9rYGCA3377DatXr0ZwcDAOHz4M4PGyWADYtm0bioqKFMcAkJ+fj3379uHAgQM4cOAAEhMTsWrVqn/8905EREQSJSciIiKSiJs3b8oByHNycuSXL1+WA5CHhoYqXePt7S03NzeXV1VVKY1bWVnJ165dK5fL5fJHjx7JTU1N5Tt27FCcHzdunHzMmDEv9NxyuVzx/JmZmc/NHRgYKG/fvr3S2IIFC+QA5KWlpX/5dcOHD5fPnTtXcdy3b1+5v7+/0jWffvqp3NXVVWns6tWrcgDy3Nzc52br27evvHfv3orj6upquYGBgXzChAmKsaKiIjkAeVpamlwul8v/9a9/yR0cHOS1tbWKazZs2CA3NDSU19TU1Pu4crlc3q1bN/mCBQsUxwDke/fuVbomKChI3qRJE/ndu3cVYwEBAfI333zzuX8WIiIiIrlcLufMMiIiItJYeXl5GDduHGxtbdG0aVPFUr3CwkLFNV27dq3zdU5OTtDV1f3Lx9XR0cHo0aPx7bffAng8i+z777+Hl5fXP3ruF3Xu3Dm8+eabSmMuLi5KxzU1Nfj000/h5OSE5s2bw9DQEAkJCc99vqysLPz8888wNDRUfLz++usA8MLLRjt27Kj4XFtbGy1atICTk5NizNzcHABw48YNxZ/HxcUFMplMcU2vXr1QXl6O33//vd7HBYBXXnlF8Rh/x9raGkZGRv/464iIiIgAQEd0ACIiIiJVcXd3h5WVFbZs2QILCwvU1tbC0dFR0bQfAAwMDOp8XX1jz/Ly8kLfvn1x48YNHD58GPr6+nBzc/tHz/0yff7551i3bh1CQ0Ph5OQEAwMDzJ49+7nPV15eDnd3d3z22Wd1zr3yyisv9NyNGjVSOpbJZEpjT4ti/3QJan2P+yKP8X/9OiIiIiKAxTIiIiLSULdv30Zubi62bNmCPn36AABSUlJe2uP37NkTlpaWiI2NxaFDh/D+++8rijQv+7nbtWuHH374QWns119/VTo+duwY3nnnHYwfPx7A48LUhQsX0L59e8U1urq6qKmpUfq6zp07Y8+ePbC2toaOjnpuDdu1a4c9e/ZALpcrCmnHjh2DkZERXn311Rd+nEaNGtX58xARERH9/+IyTCIiItJIJiYmaNGiBTZv3oyLFy/i6NGjmDNnzkt9Dk9PT4SFheHw4cNKSzBf9nP7+voiLy8PAQEByM3NRXR0NCIjI5Wusbe3x+HDh5Gamopz587hww8/xB9//KF0jbW1NX777TcUFBTg1q1bqK2txcyZM1FSUoJx48bh+PHjyM/PR0JCAiZNmqSyQtSMGTNw9epVzJo1C+fPn8f333+PoKAgzJkzB1paL357am1tjSNHjqC4uBilpaUqyUpERETSw2IZERERaSQtLS3ExMTgxIkTcHR0xMcff4zPP//8pT6Hl5cXzp49i9atW6NXr14qe+42bdpgz5492LdvH9544w2EhYUhJCRE6ZpFixahc+fOGDJkCPr164dWrVrBw8ND6Zp58+ZBW1sb7du3R8uWLVFYWAgLCwscO3YMNTU1cHV1hZOTE2bPno1mzZr9o8LVP9G6dWscPHgQ6enpeOONN+Dr64spU6Zg0aJF/+hxvvjiCxw+fBiWlpZwdnZWSVYiIiKSHplcLpeLDkFERERERERERNQQcGYZERERERERERHREyyWEREREQnm6+sLQ0PDej98fX2F5SosLPzLXIaGhigsLBSWjYiIiEhVuAyTiIiISLAbN27g7t279Z5r2rQpzMzM1JzoserqahQUFPzleXXuoElERESkLiyWERERERERERERPcFlmERERERERERERE+wWEZERERERERERPQEi2VERERERERERERPsFhGRERERERERET0BItlRERERERERERET7BYRkRERERERERE9ASLZURERERERERERE+wWEZERERERERERPTE/wPkF7jEWlYvjAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_data.plot(kind='bar',stacked=True,figsize=(15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95f5ba9b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "016a3791",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a34ca9ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dffdfc1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f8c1095b",
   "metadata": {},
   "source": [
    "# 8.. How to create some more features .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e6a54d3f",
   "metadata": {},
   "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',\n",
       "       'days_in_waiting_list', 'customer_type', 'adr',\n",
       "       'required_car_parking_spaces', 'total_of_special_requests',\n",
       "       'reservation_status', 'reservation_status_date', 'weekend_or_weekday'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52e969b4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c5ebdc70",
   "metadata": {},
   "outputs": [],
   "source": [
    "def family(row):\n",
    "    if (row['adults']>0) &  (row['children']>0 or row['babies']>0) :\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "bbe29d00",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['is_family']=data.apply(family,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8d4a6907",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['total_customer'] = data['adults'] + data['babies'] + data['children']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "5c66e415",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['total_nights']=data['stays_in_week_nights'] + data['stays_in_weekend_nights']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c9479ce4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "      <th>is_family</th>\n",
       "      <th>total_customer</th>\n",
       "      <th>total_nights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>2016</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient-Party</td>\n",
       "      <td>59.94</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>1/2/2016</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>2016</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient-Party</td>\n",
       "      <td>116.10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>1/2/2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>89.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No-Show</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          hotel  is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0  Resort Hotel            0        109               2016            January   \n",
       "1  Resort Hotel            0        109               2016            January   \n",
       "2  Resort Hotel            1          2               2016            January   \n",
       "\n",
       "   arrival_date_week_number  arrival_date_day_of_month  \\\n",
       "0                         1                          1   \n",
       "1                         1                          1   \n",
       "2                         1                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  ...  \\\n",
       "0                        0                     1       2  ...   \n",
       "1                        0                     1       2  ...   \n",
       "2                        0                     1       2  ...   \n",
       "\n",
       "   days_in_waiting_list    customer_type     adr required_car_parking_spaces  \\\n",
       "0                     0  Transient-Party   59.94                           0   \n",
       "1                     0  Transient-Party  116.10                           1   \n",
       "2                     0        Transient   89.00                           0   \n",
       "\n",
       "  total_of_special_requests reservation_status  reservation_status_date  \\\n",
       "0                         1          Check-Out                 1/2/2016   \n",
       "1                         1          Check-Out                 1/2/2016   \n",
       "2                         1            No-Show                 1/1/2016   \n",
       "\n",
       "   is_family  total_customer total_nights  \n",
       "0          0             2.0            1  \n",
       "1          1             4.0            1  \n",
       "2          0             2.0            1  \n",
       "\n",
       "[3 rows x 33 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5e798c07",
   "metadata": {},
   "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',\n",
       "       'days_in_waiting_list', 'customer_type', 'adr',\n",
       "       'required_car_parking_spaces', 'total_of_special_requests',\n",
       "       'reservation_status', 'reservation_status_date', 'is_family',\n",
       "       'total_customer', 'total_nights'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e46b9928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No Deposit', 'Non Refund', 'Refundable'], dtype=object)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['deposit_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "4dd43b95",
   "metadata": {},
   "outputs": [],
   "source": [
    "dict1={'No Deposit':0, 'Non Refund':1, 'Refundable': 0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a8ed2fa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['deposit_given']=data['deposit_type'].map(dict1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "fc17a516",
   "metadata": {},
   "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',\n",
       "       'days_in_waiting_list', 'customer_type', 'adr',\n",
       "       'required_car_parking_spaces', 'total_of_special_requests',\n",
       "       'reservation_status', 'reservation_status_date', 'is_family',\n",
       "       'total_customer', 'total_nights', 'deposit_given'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "40f6da0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['adults', 'children', 'babies', 'deposit_type'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "3a5faf2e",
   "metadata": {},
   "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', 'meal', 'country', 'market_segment',\n",
       "       'distribution_channel', 'is_repeated_guest', 'previous_cancellations',\n",
       "       'previous_bookings_not_canceled', 'reserved_room_type',\n",
       "       'assigned_room_type', 'booking_changes', 'days_in_waiting_list',\n",
       "       'customer_type', 'adr', 'required_car_parking_spaces',\n",
       "       'total_of_special_requests', 'reservation_status',\n",
       "       'reservation_status_date', 'is_family', 'total_customer',\n",
       "       'total_nights', 'deposit_given'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0f1ddfb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07696d00",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5496fcf3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d06b3d91",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "eb2fdfe8",
   "metadata": {},
   "source": [
    "# 9.. how to apply Feature encoding on data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0e7f150b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>meal</th>\n",
       "      <th>...</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",
       "      <th>is_family</th>\n",
       "      <th>total_customer</th>\n",
       "      <th>total_nights</th>\n",
       "      <th>deposit_given</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>2016</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>BB</td>\n",
       "      <td>...</td>\n",
       "      <td>Transient-Party</td>\n",
       "      <td>59.94</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>1/2/2016</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>2016</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>BB</td>\n",
       "      <td>...</td>\n",
       "      <td>Transient-Party</td>\n",
       "      <td>116.10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>1/2/2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>BB</td>\n",
       "      <td>...</td>\n",
       "      <td>Transient</td>\n",
       "      <td>89.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No-Show</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>2016</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>HB</td>\n",
       "      <td>...</td>\n",
       "      <td>Transient</td>\n",
       "      <td>73.46</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>1/3/2016</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2016</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>BB</td>\n",
       "      <td>...</td>\n",
       "      <td>Transient</td>\n",
       "      <td>119.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Canceled</td>\n",
       "      <td>12/22/2015</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>2016</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>BB</td>\n",
       "      <td>...</td>\n",
       "      <td>Transient</td>\n",
       "      <td>57.90</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Canceled</td>\n",
       "      <td>12/20/2015</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          hotel  is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0  Resort Hotel            0        109               2016            January   \n",
       "1  Resort Hotel            0        109               2016            January   \n",
       "2  Resort Hotel            1          2               2016            January   \n",
       "3  Resort Hotel            0         88               2016            January   \n",
       "4  Resort Hotel            1         20               2016            January   \n",
       "5  Resort Hotel            1         76               2016            January   \n",
       "\n",
       "   arrival_date_week_number  arrival_date_day_of_month  \\\n",
       "0                         1                          1   \n",
       "1                         1                          1   \n",
       "2                         1                          1   \n",
       "3                         1                          1   \n",
       "4                         1                          1   \n",
       "5                         1                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights meal  ...    customer_type  \\\n",
       "0                        0                     1   BB  ...  Transient-Party   \n",
       "1                        0                     1   BB  ...  Transient-Party   \n",
       "2                        0                     1   BB  ...        Transient   \n",
       "3                        0                     2   HB  ...        Transient   \n",
       "4                        0                     2   BB  ...        Transient   \n",
       "5                        1                     2   BB  ...        Transient   \n",
       "\n",
       "      adr required_car_parking_spaces  total_of_special_requests  \\\n",
       "0   59.94                           0                          1   \n",
       "1  116.10                           1                          1   \n",
       "2   89.00                           0                          1   \n",
       "3   73.46                           0                          2   \n",
       "4  119.00                           0                          0   \n",
       "5   57.90                           0                          1   \n",
       "\n",
       "   reservation_status  reservation_status_date is_family total_customer  \\\n",
       "0           Check-Out                 1/2/2016         0            2.0   \n",
       "1           Check-Out                 1/2/2016         1            4.0   \n",
       "2             No-Show                 1/1/2016         0            2.0   \n",
       "3           Check-Out                 1/3/2016         0            2.0   \n",
       "4            Canceled               12/22/2015         1            4.0   \n",
       "5            Canceled               12/20/2015         0            2.0   \n",
       "\n",
       "   total_nights  deposit_given  \n",
       "0             1              0  \n",
       "1             1              0  \n",
       "2             1              0  \n",
       "3             2              0  \n",
       "4             2              0  \n",
       "5             3              0  \n",
       "\n",
       "[6 rows x 30 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d81ada8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hotel                              object\n",
       "is_canceled                         int64\n",
       "lead_time                           int64\n",
       "arrival_date_year                   int64\n",
       "arrival_date_month                 object\n",
       "arrival_date_week_number            int64\n",
       "arrival_date_day_of_month           int64\n",
       "stays_in_weekend_nights             int64\n",
       "stays_in_week_nights                int64\n",
       "meal                               object\n",
       "country                            object\n",
       "market_segment                     object\n",
       "distribution_channel               object\n",
       "is_repeated_guest                   int64\n",
       "previous_cancellations              int64\n",
       "previous_bookings_not_canceled      int64\n",
       "reserved_room_type                 object\n",
       "assigned_room_type                 object\n",
       "booking_changes                     int64\n",
       "days_in_waiting_list                int64\n",
       "customer_type                      object\n",
       "adr                               float64\n",
       "required_car_parking_spaces         int64\n",
       "total_of_special_requests           int64\n",
       "reservation_status                 object\n",
       "reservation_status_date            object\n",
       "is_family                           int64\n",
       "total_customer                    float64\n",
       "total_nights                        int64\n",
       "deposit_given                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "381ab337",
   "metadata": {},
   "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', 'meal', 'country', 'market_segment',\n",
       "       'distribution_channel', 'is_repeated_guest', 'previous_cancellations',\n",
       "       'previous_bookings_not_canceled', 'reserved_room_type',\n",
       "       'assigned_room_type', 'booking_changes', 'days_in_waiting_list',\n",
       "       'customer_type', 'adr', 'required_car_parking_spaces',\n",
       "       'total_of_special_requests', 'reservation_status',\n",
       "       'reservation_status_date', 'is_family', 'total_customer',\n",
       "       'total_nights', 'deposit_given'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "7c028780",
   "metadata": {},
   "outputs": [],
   "source": [
    "cate_features=[col for col in data.columns if data[col].dtype=='object']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "852e22e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_features=[col for col in data.columns if data[col].dtype!='object']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "3e7888e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['is_canceled',\n",
       " 'lead_time',\n",
       " 'arrival_date_year',\n",
       " 'arrival_date_week_number',\n",
       " 'arrival_date_day_of_month',\n",
       " 'stays_in_weekend_nights',\n",
       " 'stays_in_week_nights',\n",
       " 'is_repeated_guest',\n",
       " 'previous_cancellations',\n",
       " 'previous_bookings_not_canceled',\n",
       " 'booking_changes',\n",
       " 'days_in_waiting_list',\n",
       " 'adr',\n",
       " 'required_car_parking_spaces',\n",
       " 'total_of_special_requests',\n",
       " 'is_family',\n",
       " 'total_customer',\n",
       " 'total_nights',\n",
       " 'deposit_given']"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "3a8f5d08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hotel',\n",
       " 'arrival_date_month',\n",
       " 'meal',\n",
       " 'country',\n",
       " 'market_segment',\n",
       " 'distribution_channel',\n",
       " 'reserved_room_type',\n",
       " 'assigned_room_type',\n",
       " 'customer_type',\n",
       " 'reservation_status',\n",
       " 'reservation_status_date']"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cate_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "c51c432b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cat=data[cate_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ca57065",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65a71bbe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ea89adf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'City Hotel': 0.4178593534858457, 'Resort Hotel': 0.27767373336329815}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['hotel'])['is_canceled'].mean().to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "691f94fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "81f353b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cat['cancellation']=data['is_canceled']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d9ed0f29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>customer_type</th>\n",
       "      <th>reservation_status</th>\n",
       "      <th>reservation_status_date</th>\n",
       "      <th>cancellation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>January</td>\n",
       "      <td>BB</td>\n",
       "      <td>RUS</td>\n",
       "      <td>Online TA</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>Transient-Party</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>1/2/2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>January</td>\n",
       "      <td>BB</td>\n",
       "      <td>RUS</td>\n",
       "      <td>Online TA</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>Transient-Party</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>1/2/2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>January</td>\n",
       "      <td>BB</td>\n",
       "      <td>PRT</td>\n",
       "      <td>Online TA</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>Transient</td>\n",
       "      <td>No-Show</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>January</td>\n",
       "      <td>HB</td>\n",
       "      <td>ARG</td>\n",
       "      <td>Online TA</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>Transient</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>1/3/2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>January</td>\n",
       "      <td>BB</td>\n",
       "      <td>PRT</td>\n",
       "      <td>Online TA</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>Transient</td>\n",
       "      <td>Canceled</td>\n",
       "      <td>12/22/2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          hotel arrival_date_month meal country market_segment  \\\n",
       "0  Resort Hotel            January   BB     RUS      Online TA   \n",
       "1  Resort Hotel            January   BB     RUS      Online TA   \n",
       "2  Resort Hotel            January   BB     PRT      Online TA   \n",
       "3  Resort Hotel            January   HB     ARG      Online TA   \n",
       "4  Resort Hotel            January   BB     PRT      Online TA   \n",
       "\n",
       "  distribution_channel reserved_room_type assigned_room_type    customer_type  \\\n",
       "0                TA/TO                  A                  D  Transient-Party   \n",
       "1                TA/TO                  H                  H  Transient-Party   \n",
       "2                TA/TO                  D                  D        Transient   \n",
       "3                TA/TO                  A                  D        Transient   \n",
       "4                TA/TO                  G                  G        Transient   \n",
       "\n",
       "  reservation_status reservation_status_date  cancellation  \n",
       "0          Check-Out                1/2/2016             0  \n",
       "1          Check-Out                1/2/2016             0  \n",
       "2            No-Show                1/1/2016             1  \n",
       "3          Check-Out                1/3/2016             0  \n",
       "4           Canceled              12/22/2015             1  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "56c20f5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=data_cat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "bcb4a3de",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=cols[0:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a2e09543",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['hotel', 'arrival_date_month', 'meal', 'country', 'market_segment',\n",
       "       'distribution_channel', 'reserved_room_type', 'assigned_room_type',\n",
       "       'customer_type', 'reservation_status', 'reservation_status_date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "49e11516",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Perform Mean Encoding Technique \n",
    "\n",
    "for col in cols:\n",
    "    dict2=data_cat.groupby([col])['cancellation'].mean().to_dict()\n",
    "    data_cat[col]=data_cat[col].map(dict2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a56cb2ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>customer_type</th>\n",
       "      <th>reservation_status</th>\n",
       "      <th>reservation_status_date</th>\n",
       "      <th>cancellation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.277674</td>\n",
       "      <td>0.305016</td>\n",
       "      <td>0.374106</td>\n",
       "      <td>0.379365</td>\n",
       "      <td>0.36759</td>\n",
       "      <td>0.410598</td>\n",
       "      <td>0.391567</td>\n",
       "      <td>0.251373</td>\n",
       "      <td>0.254504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.048276</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.277674</td>\n",
       "      <td>0.305016</td>\n",
       "      <td>0.374106</td>\n",
       "      <td>0.379365</td>\n",
       "      <td>0.36759</td>\n",
       "      <td>0.410598</td>\n",
       "      <td>0.407654</td>\n",
       "      <td>0.352528</td>\n",
       "      <td>0.254504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.048276</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.277674</td>\n",
       "      <td>0.305016</td>\n",
       "      <td>0.374106</td>\n",
       "      <td>0.562958</td>\n",
       "      <td>0.36759</td>\n",
       "      <td>0.410598</td>\n",
       "      <td>0.318108</td>\n",
       "      <td>0.251373</td>\n",
       "      <td>0.407864</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.101010</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      hotel  arrival_date_month      meal   country  market_segment  \\\n",
       "0  0.277674            0.305016  0.374106  0.379365         0.36759   \n",
       "1  0.277674            0.305016  0.374106  0.379365         0.36759   \n",
       "2  0.277674            0.305016  0.374106  0.562958         0.36759   \n",
       "\n",
       "   distribution_channel  reserved_room_type  assigned_room_type  \\\n",
       "0              0.410598            0.391567            0.251373   \n",
       "1              0.410598            0.407654            0.352528   \n",
       "2              0.410598            0.318108            0.251373   \n",
       "\n",
       "   customer_type  reservation_status  reservation_status_date  cancellation  \n",
       "0       0.254504                 0.0                 0.048276             0  \n",
       "1       0.254504                 0.0                 0.048276             0  \n",
       "2       0.407864                 1.0                 0.101010             1  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cat.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29bf1af9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ff60491",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1b64f26",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f87e8217",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4bce8c2d",
   "metadata": {},
   "source": [
    "# 10.. Handle Outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f6c5330d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_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>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>is_family</th>\n",
       "      <th>total_customer</th>\n",
       "      <th>total_nights</th>\n",
       "      <th>deposit_given</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>59.94</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>116.10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73.46</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>119.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119205</th>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "      <td>2016</td>\n",
       "      <td>53</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>117.82</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119206</th>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>2016</td>\n",
       "      <td>53</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90.34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119207</th>\n",
       "      <td>0</td>\n",
       "      <td>107</td>\n",
       "      <td>2016</td>\n",
       "      <td>53</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>103.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119208</th>\n",
       "      <td>0</td>\n",
       "      <td>310</td>\n",
       "      <td>2016</td>\n",
       "      <td>53</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>68.57</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119209</th>\n",
       "      <td>0</td>\n",
       "      <td>310</td>\n",
       "      <td>2016</td>\n",
       "      <td>53</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>68.57</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119210 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        is_canceled  lead_time  arrival_date_year  arrival_date_week_number  \\\n",
       "0                 0        109               2016                         1   \n",
       "1                 0        109               2016                         1   \n",
       "2                 1          2               2016                         1   \n",
       "3                 0         88               2016                         1   \n",
       "4                 1         20               2016                         1   \n",
       "...             ...        ...                ...                       ...   \n",
       "119205            0        173               2016                        53   \n",
       "119206            0         17               2016                        53   \n",
       "119207            0        107               2016                        53   \n",
       "119208            0        310               2016                        53   \n",
       "119209            0        310               2016                        53   \n",
       "\n",
       "        arrival_date_day_of_month  stays_in_weekend_nights  \\\n",
       "0                               1                        0   \n",
       "1                               1                        0   \n",
       "2                               1                        0   \n",
       "3                               1                        0   \n",
       "4                               1                        0   \n",
       "...                           ...                      ...   \n",
       "119205                         26                        3   \n",
       "119206                         30                        2   \n",
       "119207                         31                        2   \n",
       "119208                         27                        2   \n",
       "119209                         27                        2   \n",
       "\n",
       "        stays_in_week_nights  is_repeated_guest  previous_cancellations  \\\n",
       "0                          1                  0                       0   \n",
       "1                          1                  0                       0   \n",
       "2                          1                  0                       0   \n",
       "3                          2                  0                       0   \n",
       "4                          2                  0                       0   \n",
       "...                      ...                ...                     ...   \n",
       "119205                     8                  0                       0   \n",
       "119206                     5                  0                       0   \n",
       "119207                     5                  0                       0   \n",
       "119208                    10                  0                       0   \n",
       "119209                    10                  0                       0   \n",
       "\n",
       "        previous_bookings_not_canceled  booking_changes  days_in_waiting_list  \\\n",
       "0                                    0                0                     0   \n",
       "1                                    0                0                     0   \n",
       "2                                    0                0                     0   \n",
       "3                                    0                0                     0   \n",
       "4                                    0                0                     0   \n",
       "...                                ...              ...                   ...   \n",
       "119205                               0                4                     0   \n",
       "119206                               0                0                     0   \n",
       "119207                               0                0                     0   \n",
       "119208                               0                2                     0   \n",
       "119209                               0                2                     0   \n",
       "\n",
       "           adr  required_car_parking_spaces  total_of_special_requests  \\\n",
       "0        59.94                            0                          1   \n",
       "1       116.10                            1                          1   \n",
       "2        89.00                            0                          1   \n",
       "3        73.46                            0                          2   \n",
       "4       119.00                            0                          0   \n",
       "...        ...                          ...                        ...   \n",
       "119205  117.82                            0                          1   \n",
       "119206   90.34                            0                          1   \n",
       "119207  103.57                            0                          0   \n",
       "119208   68.57                            0                          2   \n",
       "119209   68.57                            0                          2   \n",
       "\n",
       "        is_family  total_customer  total_nights  deposit_given  \n",
       "0               0             2.0             1              0  \n",
       "1               1             4.0             1              0  \n",
       "2               0             2.0             1              0  \n",
       "3               0             2.0             2              0  \n",
       "4               1             4.0             2              0  \n",
       "...           ...             ...           ...            ...  \n",
       "119205          1             3.0            11              0  \n",
       "119206          0             2.0             7              0  \n",
       "119207          0             2.0             7              0  \n",
       "119208          0             2.0            12              0  \n",
       "119209          0             2.0            12              0  \n",
       "\n",
       "[119210 rows x 19 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[num_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ecf46ce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "46258d82",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe=pd.concat([data_cat,data[num_features]],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3d2802c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['hotel', 'arrival_date_month', 'meal', 'country', 'market_segment',\n",
       "       'distribution_channel', 'reserved_room_type', 'assigned_room_type',\n",
       "       'customer_type', 'reservation_status', 'reservation_status_date',\n",
       "       'cancellation', 'is_canceled', 'lead_time', 'arrival_date_year',\n",
       "       'arrival_date_week_number', 'arrival_date_day_of_month',\n",
       "       'stays_in_weekend_nights', 'stays_in_week_nights', 'is_repeated_guest',\n",
       "       'previous_cancellations', 'previous_bookings_not_canceled',\n",
       "       'booking_changes', 'days_in_waiting_list', 'adr',\n",
       "       'required_car_parking_spaces', 'total_of_special_requests', 'is_family',\n",
       "       'total_customer', 'total_nights', 'deposit_given'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "dfbe67db",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe.drop(['cancellation'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "a457e39a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>customer_type</th>\n",
       "      <th>reservation_status</th>\n",
       "      <th>...</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>is_family</th>\n",
       "      <th>total_customer</th>\n",
       "      <th>total_nights</th>\n",
       "      <th>deposit_given</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.277674</td>\n",
       "      <td>0.305016</td>\n",
       "      <td>0.374106</td>\n",
       "      <td>0.379365</td>\n",
       "      <td>0.36759</td>\n",
       "      <td>0.410598</td>\n",
       "      <td>0.391567</td>\n",
       "      <td>0.251373</td>\n",
       "      <td>0.254504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>59.94</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.277674</td>\n",
       "      <td>0.305016</td>\n",
       "      <td>0.374106</td>\n",
       "      <td>0.379365</td>\n",
       "      <td>0.36759</td>\n",
       "      <td>0.410598</td>\n",
       "      <td>0.407654</td>\n",
       "      <td>0.352528</td>\n",
       "      <td>0.254504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>116.10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.277674</td>\n",
       "      <td>0.305016</td>\n",
       "      <td>0.374106</td>\n",
       "      <td>0.562958</td>\n",
       "      <td>0.36759</td>\n",
       "      <td>0.410598</td>\n",
       "      <td>0.318108</td>\n",
       "      <td>0.251373</td>\n",
       "      <td>0.407864</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      hotel  arrival_date_month      meal   country  market_segment  \\\n",
       "0  0.277674            0.305016  0.374106  0.379365         0.36759   \n",
       "1  0.277674            0.305016  0.374106  0.379365         0.36759   \n",
       "2  0.277674            0.305016  0.374106  0.562958         0.36759   \n",
       "\n",
       "   distribution_channel  reserved_room_type  assigned_room_type  \\\n",
       "0              0.410598            0.391567            0.251373   \n",
       "1              0.410598            0.407654            0.352528   \n",
       "2              0.410598            0.318108            0.251373   \n",
       "\n",
       "   customer_type  reservation_status  ...  previous_bookings_not_canceled  \\\n",
       "0       0.254504                 0.0  ...                               0   \n",
       "1       0.254504                 0.0  ...                               0   \n",
       "2       0.407864                 1.0  ...                               0   \n",
       "\n",
       "   booking_changes  days_in_waiting_list     adr  required_car_parking_spaces  \\\n",
       "0                0                     0   59.94                            0   \n",
       "1                0                     0  116.10                            1   \n",
       "2                0                     0   89.00                            0   \n",
       "\n",
       "   total_of_special_requests  is_family  total_customer  total_nights  \\\n",
       "0                          1          0             2.0             1   \n",
       "1                          1          1             4.0             1   \n",
       "2                          1          0             2.0             1   \n",
       "\n",
       "   deposit_given  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "\n",
       "[3 rows x 30 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccf004a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "8a1d613b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='lead_time', ylabel='Density'>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dataframe['lead_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f457d15b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "43d0b24c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def handle_outlier(col):\n",
    "    dataframe[col]=np.log1p(dataframe[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "8e433b12",
   "metadata": {},
   "outputs": [],
   "source": [
    "handle_outlier('lead_time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "a900ef8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='lead_time', ylabel='Density'>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAGxCAYAAACKvAkXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/P9b71AAAACXBIWXMAAA9hAAAPYQGoP6dpAABnVElEQVR4nO3de1zUVf4/8NfMwAz3+x1BbireAAUlTbNWErtqF9esTaWy/Vb+1pZu625pl92lq2vtulqWml2t3bK2C2YUdhElIbxfkTvMcGdggBmY+fz+GBgjUbnM8JnL6/l4zGOXmc98eE+lvDjnfc6RCIIggIiIiMiBSMUugIiIiGikMQARERGRw2EAIiIiIofDAEREREQOhwGIiIiIHA4DEBERETkcBiAiIiJyOAxARERE5HCcxC7AGhkMBlRXV8PT0xMSiUTscoiIiGgABEFAa2srwsLCIJVefIyHAagf1dXViIiIELsMIiIiGoKKigqMGjXqotcwAPXD09MTgPEfoJeXl8jVEBER0UCo1WpERESYfo5fDANQP3qnvby8vBiAiIiIbMxA2lfYBE1EREQOhwGIiIiIHA4DEBERETkcBiAiIiJyOAxARERE5HAYgIiIiMjhMAARERGRw2EAIiIiIodjFQFow4YNiIqKgouLC1JTU5Gfnz+g973//vuQSCRYuHBhn+cFQcCaNWsQGhoKV1dXpKWl4fTp0xaonIiIiGyR6AFox44dyMzMxNq1a1FYWIjExESkp6ejtrb2ou8rLS3Fww8/jNmzZ5/32vPPP49XXnkFmzZtwv79++Hu7o709HR0dnZa6mMQERGRDRE9AK1btw4rVqxARkYGJkyYgE2bNsHNzQ1btmy54Hv0ej3uuOMOPPXUU4iJienzmiAIWL9+PR5//HEsWLAACQkJ2L59O6qrq7Fz504LfxoiIiKyBaIGIJ1Oh4KCAqSlpZmek0qlSEtLQ15e3gXf9/TTTyMoKAh33333ea+VlJRAqVT2uae3tzdSU1Mvek8iIiJyHKIehlpfXw+9Xo/g4OA+zwcHB+PEiRP9vueHH37AG2+8gaKion5fVyqVpnv8+p69r/2aVquFVqs1fa1Wqwf6EYiIiMgGiT4FNhitra248847sXnzZgQEBJjtvllZWfD29jY9IiIizHZvIiIisj6ijgAFBARAJpNBpVL1eV6lUiEkJOS864uLi1FaWoobbrjB9JzBYAAAODk54eTJk6b3qVQqhIaG9rlnUlJSv3WsXr0amZmZpq/VajVDEBERkR0TNQDJ5XIkJycjJyfHtJTdYDAgJycHK1euPO/6+Ph4HD58uM9zjz/+OFpbW/Hyyy8jIiICzs7OCAkJQU5OjinwqNVq7N+/H/fdd1+/dSgUCigUCrN+Nmvx7v7yi75+e2rkCFVCRERkPUQNQACQmZmJZcuWISUlBdOnT8f69euh0WiQkZEBAFi6dCnCw8ORlZUFFxcXTJo0qc/7fXx8AKDP8w8++CD++te/YsyYMYiOjsYTTzyBsLCw8/YLIiIiIsckegBavHgx6urqsGbNGiiVSiQlJSE7O9vUxFxeXg6pdHCtSo8++ig0Gg3uvfdeNDc3Y9asWcjOzoaLi4slPgIRERHZGIkgCILYRVgbtVoNb29vtLS0wMvLS+xyhoVTYERE5CgG8/PbplaBEREREZkDAxARERE5HAYgIiIicjgMQERERORwGICIiIjI4TAAERERkcNhACIiIiKHwwBEREREDocBiIiIiBwOAxARERE5HAYgIiIicjgMQERERORwGICIiIjI4TAAERERkcNhACIiIiKHwwBEREREDocBiIiIiBwOAxARERE5HAYgIiIicjgMQERERORwGICIiIjI4TAAERERkcNhACIiIiKHwwBEREREDocBiIiIiBwOAxARERE5HAYgIiIicjgMQERERORwGICIiIjI4TAAERERkcNhACIiIiKHwwBEREREDsdJ7AKIiIh6vbu//KKv354aOUKVkL2zihGgDRs2ICoqCi4uLkhNTUV+fv4Fr/3oo4+QkpICHx8fuLu7IykpCW+99Vafa5YvXw6JRNLnMX/+fEt/DCIiIrIRoo8A7dixA5mZmdi0aRNSU1Oxfv16pKen4+TJkwgKCjrvej8/P/zlL39BfHw85HI5PvvsM2RkZCAoKAjp6emm6+bPn4+tW7eavlYoFCPyeYiIiMj6iT4CtG7dOqxYsQIZGRmYMGECNm3aBDc3N2zZsqXf66+88krcdNNNGD9+PGJjY7Fq1SokJCTghx9+6HOdQqFASEiI6eHr6zsSH4eIiIhsgKgBSKfToaCgAGlpaabnpFIp0tLSkJeXd8n3C4KAnJwcnDx5EldccUWf13JzcxEUFIRx48bhvvvuQ0NDg9nrJyIiItsk6hRYfX099Ho9goOD+zwfHByMEydOXPB9LS0tCA8Ph1arhUwmw7///W9cffXVptfnz5+Pm2++GdHR0SguLsaf//xnXHPNNcjLy4NMJjvvflqtFlqt1vS1Wq02w6cjIiIiayV6D9BQeHp6oqioCG1tbcjJyUFmZiZiYmJw5ZVXAgBuu+0207WTJ09GQkICYmNjkZubi7lz5553v6ysLDz11FMjVT4RERGJTNQpsICAAMhkMqhUqj7Pq1QqhISEXPB9UqkUcXFxSEpKwkMPPYRbb70VWVlZF7w+JiYGAQEBOHPmTL+vr169Gi0tLaZHRUXF0D4QERER2QRRA5BcLkdycjJycnJMzxkMBuTk5GDGjBkDvo/BYOgzhfVrlZWVaGhoQGhoaL+vKxQKeHl59XkQERGR/RJ9CiwzMxPLli1DSkoKpk+fjvXr10Oj0SAjIwMAsHTpUoSHh5tGeLKyspCSkoLY2FhotVp88cUXeOutt7Bx40YAQFtbG5566inccsstCAkJQXFxMR599FHExcX1WSZPREREjkv0ALR48WLU1dVhzZo1UCqVSEpKQnZ2tqkxury8HFLpuYEqjUaD+++/H5WVlXB1dUV8fDzefvttLF68GAAgk8lw6NAhvPnmm2hubkZYWBjmzZuHZ555hnsBEREREQBAIgiCIHYR1katVsPb2xstLS02Px3GbeWJyJbw7ywajsH8/BZ9I0QiIiKikcYARERERA5H9B4gIiKigeIUGZkLR4CIiIjI4TAAERERkcNhACIiIqskCAJaO7tg4GJlsgD2ABERkVWpa9Xi+9N1OF3bhpaOLvi5yzErLgDJo33hLOPv7WQeDEBERGQ1zta14e39ZejsMpiea9To8OnBauw724D/mxMLF2eZiBWSvWAAIiIiq/DZoWps3VsKvUHAaD83XBUfhHAfVxyqbMa3J+tQ26rFBwcq8LvLRkMqkYhdLtk4jiUSEZHoiiqa8ccdRdAbBEwM88Jds6IxNtgT7gonzIgNwLIZUXCSSnBC2YpvT9SKXS7ZAQYgIiISVXO7Dg+8U4guvTH8LJkeeV6vT7ivKxYmhQMAck7UoqKxXYxSyY4wABERkWgEQcBDHxxEVXMHRvu74Zapoy44vTV1tC+SInwAAN9wFIiGiT1AREQ0YObeifmDAxXIOVELuZMU/75jKg5WtFz0+rnxQThY0YyTqlZUNrVjlK/boL4fUS+OABERkSjqWrX42+fHAQAPzxuLiWHel3yPv4eCo0BkFgxAREQkiqc/OwZ1ZzcmhXvhrsujB/y+q8YFQQLghLIVVc0dliuQ7BoDEBERjbhvT9TifwerIZUAz96cAKdBbHAY4KlAwijjaNG+4gZLlUh2jgGIiIhGlEbbjcd3HgEA3D0rGpPCLz319Wup0f4AgMNVLdB2681aHzkGBiAiIhpR63afQlVzB8J9XPHHq8cO6R6j/d3g5y6HTm/A0Wq1mSskR8BVYERENGKezz6BLT+UAADSxgdj58/VQ7qPRCLB1EhffH1chcKyJkyN9DVnmeQAOAJEREQjQtdtwEeFVRAAJIzyxrgQz2Hdb2qkDyQAztZr0KTRmaVGchwMQERENCI2fHsGSnUn3OQyXJ8QNuz7+bjJERPoDgAorGga9v3IsTAAERGRxR2tbsGGb88AAG5MDIOHwjwdGL1TX5faQJHo1xiAiIjIojq79Hj4w0Po7jnodPIQVn1dyPhQL8ikEtS3aVGr7jTbfcn+MQAREZFFPfPZMRyvUcPPXY4bE8MgucBZX0Ph4ixDXKAHAOBoDVeD0cAxABERkcV8UlSFd/aXQyIB/rE4CZ4uzmb/HhPCvAAAx7gcngaBAYiIiCzieI0af/7oMABg5VVxmDM20CLfZ3yoFyQAqpo7UNnUbpHvQfaHAYiIiMyuorEdy7bkQ6PTY0aMPx5MG9qGhwPhoXBCVIBxNdhXR1UW+z5kXxiAiIjIrBratFi2NR+1rVqMC/bEpjuTIZOar++nPxNCjdNg2UeVFv0+ZD8YgIiIyGwqGtux6NU8nK3TIMzbBdvumgZvV/P3/fzaxJ4+oAOljWjkpog0AAxARERkFtXNHbh5415T+Nl+dypCvV1H5Hv7uMkR6u0CgwDknqwdke9Jto0BiIiIhu1MbRs2f38Wda1axId44qP7L0dckMeI1hDfc7RGzgkGILo0HoZKRETDUlTRhP8WVEEvCLgsxg+vLU2BlwWWu19KfIgXvj1Zh6+PqfBWXtl5fUe3p0aOeE1kvTgCREREQyIIAr47VYcPDlRCLwiYHO6NN++aLkr4AYBwX1e4y2XQdhtQ2qARpQayHQxAREQ0aAZBwOeHa0yrri6P9cfiaRFQOMlEq0kqkWBciLEZ+qSyVbQ6yDZYRQDasGEDoqKi4OLigtTUVOTn51/w2o8++ggpKSnw8fGBu7s7kpKS8NZbb/W5RhAErFmzBqGhoXB1dUVaWhpOnz5t6Y9BROQQDIKAj3+uwt7iBgDANZNCcF1CGKRmPOJiqHr7gE4ouSs0XZzoAWjHjh3IzMzE2rVrUVhYiMTERKSnp6O2tv8mNj8/P/zlL39BXl4eDh06hIyMDGRkZGDXrl2ma55//nm88sor2LRpE/bv3w93d3ekp6ejs5MH5RERDYdBEPBRYSUKypogAbAoeRRmj7HMDs9DERfkAZlEgvo2HepbtWKXQ1ZM9AC0bt06rFixAhkZGZgwYQI2bdoENzc3bNmypd/rr7zyStx0000YP348YmNjsWrVKiQkJOCHH34AYBz9Wb9+PR5//HEsWLAACQkJ2L59O6qrq7Fz584R/GRERPZFEAR8WlSNwvJmSCXA4mkRmBLpK3ZZfbg4yxDdsys0R4HoYkQNQDqdDgUFBUhLSzM9J5VKkZaWhry8vEu+XxAE5OTk4OTJk7jiiisAACUlJVAqlX3u6e3tjdTU1AveU6vVQq1W93kQEVFfm78/i/zSRkgALJ4WiYRRPmKX1K9xpmkw9gHRhYkagOrr66HX6xEcHNzn+eDgYCiVF97OvKWlBR4eHpDL5bjuuuvwz3/+E1dffTUAmN43mHtmZWXB29vb9IiIiBjOxyIisjvZR2qQ9eUJAMC1k0MxOdxb5IourLcPqLRBgw6dXuRqyFqJPgU2FJ6enigqKsJPP/2Ev/3tb8jMzERubu6Q77d69Wq0tLSYHhUVFeYrlojIxpXUa/DQBwchCMBlMf6YGesvdkkX5e+hQKCnAgYBOF3LUSDqn6gbIQYEBEAmk0Gl6nt6r0qlQkhIyAXfJ5VKERcXBwBISkrC8ePHkZWVhSuvvNL0PpVKhdDQ0D73TEpK6vd+CoUCCoVimJ+GiMj+aLv1WPluITQ6PVKj/XDd5FBIrGC116XEh3iirlWLk8pWq52qI3GJOgIkl8uRnJyMnJwc03MGgwE5OTmYMWPGgO9jMBig1Rq7/aOjoxESEtLnnmq1Gvv37x/UPYmICMj64gSOVqvh5y7Hy7dNsfip7uYS37sfkKoVBkEQuRqyRqIfhZGZmYlly5YhJSUF06dPx/r166HRaJCRkQEAWLp0KcLDw5GVlQXA2K+TkpKC2NhYaLVafPHFF3jrrbewceNGAIBEIsGDDz6Iv/71rxgzZgyio6PxxBNPICwsDAsXLhTrYxIR2ZzvT9dh295SAMCLixIQ4u0ibkGDEOnnBhdnKdp1elQ0tmO0v7vYJZGVET0ALV68GHV1dVizZg2USiWSkpKQnZ1tamIuLy+HVHpuoEqj0eD+++9HZWUlXF1dER8fj7fffhuLFy82XfPoo49Co9Hg3nvvRXNzM2bNmoXs7Gy4uNjOH14iIjFptN34038PAwCWzhiN38QHX+Id1kUmlWBssCcOVbbghLKVAYjOIxEEjg3+mlqthre3N1paWuDl5SV2OcPy7v7yi77OwwGJqD9rPzmCN/PKEO7jiq/+eAXcFcbfl4f7d8ql3m9ORRXN+OBABUK8XPCHuWP4950DGMzPb5tcBUZERJZzoLQRb+aVAQCevWWyKfzYmrHBHpAAUKo70dSuE7scsjIMQEREZNKtN+DxnUcAWN8xF4PlJnfCaH83ANwUkc7HAERERCbb88pwQtkKb1dnrL52vNjlDJtpNRiPxaBfYQAiIiIAQK26E//YfQoA8Oj8cfBzl4tc0fD1HotRXKeBRtstcjVkTWxzYpeIiMwu68sTaNV2I3GUN26bNrSG4ZFsch6IIE8FfN2c0dTehR/P1GPexAtvskuOhQGIiMiODHWV1v6zDfj45ypIJMDTCybZzIaHlyKRSBAf4oW8sw345kQtAxCZcAqMiMjBdekNeOITY+PzkumRSIzwEbcgM+s9HPXr4yroDdz5hYwYgIiIHNybe0txStUGXzdnPJo+TuxyzC4m0AOuzjLUt+mwv6RB7HLISnAKjIjIgal+0fj8p2vi4eNm+43PvyaTSjAxzAsHyprw+aEazIwN6Pc6bhzrWDgCRETkwP76+XFodHpMifTBouQIscuxmMnh3gCA7CNKdOsNIldD1oABiIjIQe09U4//HayGVAI8s2ASpHbS+NyfmEAP+Lo5o0Gjw/6SRrHLISvAAERE5IB03Qas+fQoAOB3l43GpJ4REnslk0owf5JxBdhnh2pEroasAQMQEZEDeu27YpypbYO/uxwPXW1/jc/9uW5yGAAg+0gNujgN5vDYBE1E5GDO1rXhlW/OAACeuH4CvN2cTa9Z20aG5nRZjB8CPOSob9Nhz8k6pE0IFrskEhEDEBGRA3lnXxne+KEEum4DxgR5QKPttuvQ80tOMikWJoXj9R9K8GFBBQOQg+MUGBGRAyksb8bZeg2cZRIsSAqHRGK/jc/9uTVlFAAg53gtGtq0IldDYmIAIiJyEG3abnxx2NgAPDc+2C4OOx2s+BAvTA73RrdBwCdF1WKXQyJiACIichBfHK5BR5ceod4uuDyu/80AHcGtycZRoP8UVIpcCYmJAYiIyAGcVrWiqKIZEgA3TQm3m8NOh2JBUhjkMimO1ahxtLpF7HJIJAxARER2TtdtwM6iKgDAjFh/jPJ1E7kicfm4yXH1RGMDtKM0gNP5GICIiOzcNydUaGrvgrerM64ez5VPAPC71NEAgI9/rkJrZ5fI1ZAYGICIiOxYdXMHfjhTDwC4MTEMCmeZyBVZh8ti/BAX5IF2nR4f/1wldjkkAgYgIiI7ZRAE7CyqgkEAJoV7Y3yol9glWQ2JRII7LzOOAr2VVwZBEESuiEYaAxARkZ3KL2lEZVMHFE5SXJ8QKnY5VuemqeFwk8twuraNB6Q6IAYgIiI71NrZhV1HlQCA9Ikh8HJxvsQ7HI+XizMWTgkHALy1r0zkamikMQAREdmhLw7XQNttQLiPK6ZH+4ldjtXqbYbedUQJNZuhHQoDEBGRnTlT24aDlS2QAFiYFA6pgx13MRgTwryQMtoX3QYBB0o5DeZIGICIiOxIl96AT3r2/Lksxh/hvq4iV2T97pxhHAXKL2mE3sBmaEfBAEREZEe+O12HBo0OngonXM3Tzgdk/qQQ+LvLoe7sxvEatdjl0AhhACIishOl9RrsOVkHALguIRQu3PNnQBROMiyeFgEA2F/SIHI1NFKcxC6AiIj6utjxDLenRl7wtb99cRzdBgFxQR6YHO5tidLs1u2pkdiYW4ziOg1qWzsR5OkidklkYRwBIiKyA/vPNmD3MRWkEuD6yaGQsPF5UEb5umFciCcAYy8Q2T8GICIiG2cwCPj7F8cBAClRfgjy4ujFUFwW4w8AKCxvgq7bIHI1ZGlWEYA2bNiAqKgouLi4IDU1Ffn5+Re8dvPmzZg9ezZ8fX3h6+uLtLS0865fvnw5JBJJn8f8+fMt/TGIiETx2eEaHKxsgbtchrnxQWKXY7Pigjzg5y5HZ5cBByubxS6HLEz0ALRjxw5kZmZi7dq1KCwsRGJiItLT01FbW9vv9bm5uViyZAm+/fZb5OXlISIiAvPmzUNVVd/D7ObPn4+amhrT47333huJj0NENKK03Xo8n30CAPB/c2LhyR2fh0wqkSC1Z9PIfWcbeD6YnRM9AK1btw4rVqxARkYGJkyYgE2bNsHNzQ1btmzp9/p33nkH999/P5KSkhAfH4/XX38dBoMBOTk5fa5TKBQICQkxPXx9fUfi4xARjajte8tQ2dSBYC8F7pkdI3Y5Ni850hdOUglqWjpR3dwpdjlkQaIGIJ1Oh4KCAqSlpZmek0qlSEtLQ15e3oDu0d7ejq6uLvj59d3qPTc3F0FBQRg3bhzuu+8+NDRceGmjVquFWq3u8yAisnbN7Tr885vTAICH5o2Dq5zL3ofLTeGE+J5m6ENVzeIWQxYlagCqr6+HXq9HcHDfzbqCg4OhVCoHdI/HHnsMYWFhfULU/PnzsX37duTk5OC5557Dnj17cM0110Cv1/d7j6ysLHh7e5seERERQ/9QREQj5J/fnIG6sxvxIZ64ZeooscuxGwmjfAAAhytbOA1mx2x6H6Bnn30W77//PnJzc+Hicm7Vw2233Wb6/5MnT0ZCQgJiY2ORm5uLuXPnnnef1atXIzMz0/S1Wq1mCCIiq1bZ1I7teaUAgD9fOx4yKZe9m8u4EE/InaRo7uhCRWM7Iv3dxS6JLEDUEaCAgADIZDKoVKo+z6tUKoSEhFz0vS+++CKeffZZfPXVV0hISLjotTExMQgICMCZM2f6fV2hUMDLy6vPg4jImv3rmzPo0guYGeuPK8YGil2OXXGWSTEh1Phz4GBVi8jVkKWIGoDkcjmSk5P7NDD3NjTPmDHjgu97/vnn8cwzzyA7OxspKSmX/D6VlZVoaGhAaGioWeomIhJTWYMGHxZUAgAemjdW5GrsU0LPTtpHqlpg4DSYXRJ9FVhmZiY2b96MN998E8ePH8d9990HjUaDjIwMAMDSpUuxevVq0/XPPfccnnjiCWzZsgVRUVFQKpVQKpVoa2sDALS1teGRRx7Bvn37UFpaipycHCxYsABxcXFIT08X5TMSEZnTyzmnoTcImDM2EMmj/S79Bhq0uGAPuDhL0drZjdJ6jdjlkAWI3gO0ePFi1NXVYc2aNVAqlUhKSkJ2drapMbq8vBxS6bmctnHjRuh0Otx666197rN27Vo8+eSTkMlkOHToEN588000NzcjLCwM8+bNwzPPPAOFQjGin42IyNyK69qw82fjvmd/vJqjP5biJJViQqg3CsubcELZiphAD7FLIjMTPQABwMqVK7Fy5cp+X8vNze3zdWlp6UXv5erqil27dpmpMiIi6/Ly16dhEIC08UFIivARuxy7NjbYA4XlTTilasW1k9lCYW9EnwIjIqKBUao78b9D1QCAB9M4+mNpcUEekACobdWiuV0ndjlkZgxAREQ2Iue4CoIAzJ8Ygkk9TbpkOW5yJ0T4uQEATte2iVwNmRsDEBGRDahu7sDRajUkEvb+jKQxwcben1OqVpErIXNjACIisgFfHzful3Z9QhjG9RzVQJY3Nsj4z/pMbRu69AaRqyFzYgAiIrJyFY3tOKFshQTAg2ljxC7HoYT7usLVWQZttwFFFc1il0NmxABERGTldh8zjv5MifRFLJdjjyipRGKaBvvuVJ3I1ZA5DSkAnT171tx1EBFRP87WteFMXRtkEgnmxgeJXY5DGhNkDEB7ixtEroTMaUgBKC4uDldddRXefvttdHZ2mrsmIiICIAiCafQnJcoXvu5ykStyTFE9h6EermxBZ5de5GrIXIYUgAoLC5GQkIDMzEyEhITg97//PfLz881dGxGRQztd24ayxnY4SSW4ahxHf8Ti5y6Hu8IJOr0BR3g4qt0Y0k7QSUlJePnll/HSSy/h008/xbZt2zBr1iyMHTsWd911F+68804EBvJ0YiKiofrl6M9lMf7wcnUWuSLHJZFIMNrPDcdq1Hj9+xKcUvW/J9DtqZEjXBkNx7CaoJ2cnHDzzTfjww8/xHPPPYczZ87g4YcfRkREBJYuXYqamhpz1UlE5FCO1ahR1dwBuUyKK8byF0qxjfY3bohY1sCDUe3FsALQgQMHcP/99yM0NBTr1q3Dww8/jOLiYuzevRvV1dVYsGCBueokInIYhl+M/syM84eHwiqObXRoo3t2hC5rbIcgCCJXQ+YwpD9V69atw9atW3Hy5Elce+212L59O6699lrTqe3R0dHYtm0boqKizFkrEZFDKChrQm2rFi7OUsyO4+iPNQjzcYWTVIJ2nR4NbToEeCrELomGaUgBaOPGjbjrrruwfPlyhIb2f0JuUFAQ3njjjWEVR0TkaNq03abRn9/EB8NVLhO5Ivvx7v7yIb/XSSZFuK8ryhraUdaoYQCyA0MKQLt370ZkZKRpxKeXIAioqKhAZGQk5HI5li1bZpYiiYgcxb+/PYM2bTf83eW4LMZP7HLoF0b7uRsDUEM7kkfz342tG1IPUGxsLOrr6897vrGxEdHR0cMuiojIEVU0tuP1H0oAANdODoWTlJv1W5NzjdDtIldC5jCkEaALNYC1tbXBxcVlWAURETkiQRDw+M4j0HUbEBPojvgLHHg6nGkcGp6InkboujYtOrv0cHHm9KQtG1QAyszMBGDcE2HNmjVwc3MzvabX67F//34kJSWZtUAiIkfw6cFq7DlVB7mTFAsSwyGRSMQuiX7FQ+EEb1dntHR0oaalE9EB7mKXRMMwqAD0888/AzD+pnL48GHI5ee2ZZfL5UhMTMTDDz9s3gqJiOxck0aHp/93DADw/66Kg78HG2ytVZiPK1o6ulDd3MEAZOMGFYC+/fZbAEBGRgZefvlleHl5WaQoIiJHIQgCnvjkCBo0OowL9sTv58TiPwWVYpdFFxDm44LjNWpUN3eIXQoN05B6gLZu3WruOoiIHNL7P1Xgs0M1cJJK8NytCZA7sfHZmoV5uwIAqlsYgGzdgAPQzTffjG3btsHLyws333zzRa/96KOPhl0YOYZLNXTybB2yRQP97/p4jRpPfnoUAPBI+jgkRfhYujQapjAfYwCqVWuh6zYwsNqwAQcgb29vU1Oet7e3xQoiInIEDW1a3Pd2AbTdBlw5LhArZseIXRINgJeLE9wVTtBou6FSd5pWhpHtGXAA+uW0F6fAiIiGTqPtxl3bfkJpQzvCfVzx0qJESKVc9WULJBIJwn1ccErVhqrmDgYgGzaksbuOjg60t5/bCKqsrAzr16/HV199ZbbCiIjsUZfegPvfKcTByhb4ujlj+93TuerLxpj6gNgIbdOGFIAWLFiA7du3AwCam5sxffp0vPTSS1iwYAE2btxo1gKJiOxFh06PrT+WYM+pOrg6y7Bl+TTEBnqIXRYNUm8fUE1Lp8iV0HAMKQAVFhZi9uzZAID//Oc/CAkJQVlZGbZv345XXnnFrAUSEdmDhjYtXv2uGKUN7fBUOGHL8mmYEukrdlk0BL0BSKnuRLfBIHI1NFRDWgbf3t4OT0/jNu1fffUVbr75ZkilUlx22WUoKysza4FERLZMEATklzbii8M16NIL8HJxwo7fz8D4UO6jZqt83Zzh4ixFZ5cBtWqtKRCRbRnSCFBcXBx27tyJiooK7Nq1C/PmzQMA1NbWcnNEIqIeLR1d2La3FJ8UVaNLLyAmwB3/NyeW4cfGSSQShHgZQ49KzWkwWzWkALRmzRo8/PDDiIqKQmpqKmbMmAHAOBo0ZcoUsxZIRGSLDlY245Wc0zhd2wYnqQTXTQ7FXbOi4eMmv/SbyeoFexkb11VqrciV0FANaQrs1ltvxaxZs1BTU4PExETT83PnzsVNN91ktuKIiGxNk0aH9/LLcbiqBQAQ7uOKRcmjEOTlInJlZE7BPf8+OQJku4YUgAAgJCQEISEhfZ6bPn36sAsiIrJV+SWNWPluIWpbtZBKgCvHBeGqcUGQcY8fu2MKQK0MQLZqSAFIo9Hg2WefRU5ODmpra2H4VRf82bNnzVIcEZEtEAQBb/xQgqwvT0BvEBDgocBvU0ZhlC83ybNXwZ7GKbDm9i50dunh4iwTuSIarCEFoHvuuQd79uzBnXfeidDQUNMRGUO1YcMGvPDCC1AqlUhMTMQ///nPC44mbd68Gdu3b8eRI0cAAMnJyfj73//e53pBELB27Vps3rwZzc3NuPzyy7Fx40aMGTNmWHUSEQF9z/oyCAI+PViN/JJGAEDiKG/cNGUUz4iyc24KJ3i6OKG1sxu1rVpEckdomzOkAPTll1/i888/x+WXXz7sAnbs2IHMzExs2rQJqampWL9+PdLT03Hy5EkEBQWdd31ubi6WLFmCmTNnwsXFBc899xzmzZuHo0ePIjw8HADw/PPP45VXXsGbb76J6OhoPPHEE0hPT8exY8fg4sJ5eCIyD71BwEeFlfi5ohkSANclhGJGjP+wfykk2xDs5YLWzjbUqjsZgGzQkH5F8fX1hZ+fn1kKWLduHVasWIGMjAxMmDABmzZtgpubG7Zs2dLv9e+88w7uv/9+JCUlIT4+Hq+//joMBgNycnIAGEd/1q9fj8cffxwLFixAQkICtm/fjurqauzcudMsNRMRCYKAj382hh+pBPhtSgRmxgYw/DiQ3mkwNkLbpiEFoGeeeQZr1qzpcx7YUOh0OhQUFCAtLe1cQVIp0tLSkJeXN6B7tLe3o6uryxTISkpKoFQq+9zT29sbqampA74nEdGlfHeqDoXlxvCzZHokEiN8xC6JRti5lWBcCm+LhjQF9tJLL6G4uBjBwcGIioqCs7Nzn9cLCwsHdJ/6+nro9XoEBwf3eT44OBgnTpwY0D0ee+wxhIWFmQKPUqk03ePX9+x97de0Wi202nP/AavV6gF9byJyTMeqW/DVMRUA4PqEMEwM8xa5IhIDl8LbtiEFoIULF5q5jKF59tln8f777yM3N3dYvT1ZWVl46qmnzFgZEdmrisZ2fFBQCQHAZTF+uCzGX+ySSCRBPVNgrdpuaLTdIldDgzWkALR27VqzfPOAgADIZDKoVKo+z6tUqvP2GPq1F198Ec8++yy+/vprJCQkmJ7vfZ9KpUJoaGifeyYlJfV7r9WrVyMzM9P0tVqtRkRExGA/DhHZOYNBwEMfHoSu24DR/m64bnKY2CWRiBTOMvi6OaOpvYv7AdmgIa/TbG5uxuuvv47Vq1ejsdG4/LOwsBBVVVUDvodcLkdycrKpgRmAqaG593iN/jz//PN45plnkJ2djZSUlD6vRUdHIyQkpM891Wo19u/ff8F7KhQKeHl59XkQEf3alh9LkF/SCLlMikXJEdzgkNgHZMOGNAJ06NAhpKWlwdvbG6WlpVixYgX8/Pzw0Ucfoby8HNu3bx/wvTIzM7Fs2TKkpKRg+vTpWL9+PTQaDTIyMgAAS5cuRXh4OLKysgAAzz33HNasWYN3330XUVFRpr4eDw8PeHh4QCKR4MEHH8Rf//pXjBkzxrQMPiwszGqm7ojI9pTUa/D8rpMAgGsnh8LPnWd6kXEa7ISyFXWtDEC2ZkgBKDMzE8uXL8fzzz8PT09P0/PXXnstbr/99kHda/Hixairq8OaNWugVCqRlJSE7OxsUxNzeXk5pNJzA1UbN26ETqfDrbfe2uc+a9euxZNPPgkAePTRR6HRaHDvvfeiubkZs2bNQnZ2NvcAIqIhe+azY9B1GzB7TACmRfmKXQ5ZiQAPYx9QPQOQzRlSAPrpp5/w6quvnvd8eHj4BVdaXczKlSuxcuXKfl/Lzc3t83Vpaekl7yeRSPD000/j6aefHnQtRES/9u3JWnxzohZOUgmeunEi9p1tFLskshKBPY3QdW0MQLZmSD1ACoWi36Xip06dQmBg4LCLIiKyFl16A5757BgAIOPyKMQEeohcEVmTwJ4RoJaOLrTruBLMlgwpAN144414+umn0dXVBcA44lJeXo7HHnsMt9xyi1kLJCIS09v7ynC2TgN/dzn+31yeJ0h9uSmc4CY3HoR6tk4jcjU0GEMKQC+99BLa2toQGBiIjo4OzJkzB3FxcfD09MTf/vY3c9dIRCSKdl03Nnx7BgCQOW8svFycL/EOckS9o0DFdW0iV0KDMaQeIG9vb+zevRs//vgjDh48iLa2NkydOrXP8RNERLbu7X1lqG/TIcLPFb9N4d5g1L9ATwXKGts5AmRjBh2ADAYDtm3bho8++gilpaWQSCSmvXcEQeBBgERkFzTabmzacxYA8IffjIGzbMjbppGdC+AIkE0a1J9oQRBw44034p577kFVVRUmT56MiRMnoqysDMuXL8dNN91kqTqJiEbUtr2laNToEOXvhpumhItdDlmx3pVgHAGyLYMaAdq2bRu+++475OTk4Kqrrurz2jfffIOFCxdi+/btWLp0qVmLJCIaSR06Pd74oQQAsCptDJzMPPrz7v5ys96PxNXbA3S2vg0GgwApdwi3CYP6U/3ee+/hz3/+83nhBwB+85vf4E9/+hPeeecdsxVHRCSGDw5UoFFj7P25IYHnfdHF+brLIZNI0NllQA1PhrcZgwpAhw4dwvz58y/4+jXXXIODBw8OuygiIrF06w3Y/L2x9+fe2TFmH/0h+yOTSkxHoxTXsg/IVgzqT3ZjY6PpiIr+BAcHo6mpadhFERGJ5fPDNahs6oC/uxyLuPKLBuhcHxADkK0YVADS6/Vwcrpw25BMJkN3N3fCJCLbJAgCXu1Z+bVsZhRcnGUiV0S24txKMDZC24pBNUELgoDly5dDoVD0+7pWy7NQiMj6XagJubiuDcdq1HCWSeAml7FZmQbMNAJUzxEgWzGoALRs2bJLXsMVYERkq/aeqQcATI30hZt8SPvEkoMK9DD2AHEpvO0Y1J/wrVu3WqoOIiJRNWp0OKFsBQDMiPUXuRqyNQE9I0A1LZ3QaLvhrmCAtnZc3kBEBCCvuB4CgDFBHgjydBG7HLIxbnIn+PesBCup5yiQLWAAIiKHp+3S40CZcQXrzNgAkashWxUb6AGAR2LYCgYgInJ4hRXN0HYbEOAhx5hgD7HLIRsVE+gOgCvBbAUDEBE5NIMgIK/Y2Pw8I8YfUh7oTEPEESDbwgBERA7ttKoN9W06KJykmBrpK3Y5ZMN6R4C4Esw2MAARkUPLO2sc/UkZ7QsFNz6kYegdASrpORSVrBsDEBE5rNrWTpxStUECYAabn2mYRvm6wllmPBS1uqVD7HLoEhiAiMhh5RU3AADiQzxNh1kSDZWTTIoofzZC2woGICJySO26bhSW9yx9j+PoD5nHuT4gNkJbO25VSUR2ZyBneP1U0oguvYAQLxfEBLiPQFXkCIx9QCquBLMBHAEiIoejNwjIO2uc/ro8LgASLn0nM4npaYTmSjDrxwBERA7ncFUL1J3d8FA4IXGUt9jlkB2JNW2GyBEga8cAREQORRAE/Nhz6vtlMX5wkvGvQTKf3hEglVqLNm23yNXQxfBPPhE5lLKGdlQ1d8BJKsH0aJ76Tubl7eqMAI+eQ1E5DWbVGICIyKH80DP6kxThAw8F14GQ+cUE9PQB1XMazJoxABGRw2jU6HC8Rg3A2PxMZAmmQ1FrGYCsGQMQETmMvcX1EACMCfJAsJeL2OWQnTIFoHpOgVkzBiAicggdOj0OlBk3PuToD1lSLJfC2wQGICJyCHln66HrNiDIU4ExQR5il0N2LIaHotoE0QPQhg0bEBUVBRcXF6SmpiI/P/+C1x49ehS33HILoqKiIJFIsH79+vOuefLJJyGRSPo84uPjLfgJiMjadXbp8eMZ48aHV8UHceNDsqiIXxyKWqPuFLscugBRA9COHTuQmZmJtWvXorCwEImJiUhPT0dtbW2/17e3tyMmJgbPPvssQkJCLnjfiRMnoqamxvT44YcfLPURiMgG7DvbgI4uPQI9FJgczo0PybKcZFJE+rkB4Jlg1kzUALRu3TqsWLECGRkZmDBhAjZt2gQ3Nzds2bKl3+unTZuGF154AbfddhsUCsUF7+vk5ISQkBDTIyCA8/1EjkrbrTctfb8qPhBSjv7QCOCRGNZPtACk0+lQUFCAtLS0c8VIpUhLS0NeXt6w7n369GmEhYUhJiYGd9xxB8rLL30wIhHZp73FDWjX6eHvLsfkcB+xyyEHEcMjMayeaAGovr4eer0ewcHBfZ4PDg6GUqkc8n1TU1Oxbds2ZGdnY+PGjSgpKcHs2bPR2tp6wfdotVqo1eo+DyKyfa2dXdhzqg4AkDY+GDIpR39oZMQGcATI2tndNqjXXHON6f8nJCQgNTUVo0ePxgcffIC777673/dkZWXhqaeeGqkSiWiEfH28FrpuA0b5uiKBh57SCIoNMo4AsQfIeok2AhQQEACZTAaVStXneZVKddEG58Hy8fHB2LFjcebMmQtes3r1arS0tJgeFRUVZvv+RCQOpboTB0obAQDXTQ7lyi8aUb3HYVS3dKJdx0NRrZFoAUgulyM5ORk5OTmm5wwGA3JycjBjxgyzfZ+2tjYUFxcjNDT0gtcoFAp4eXn1eRCR7TIIAv53sBoCgIlhXhjt7y52SeRgfN3l8HVzBgCUcEdoqyTqKrDMzExs3rwZb775Jo4fP4777rsPGo0GGRkZAIClS5di9erVput1Oh2KiopQVFQEnU6HqqoqFBUV9Rndefjhh7Fnzx6UlpZi7969uOmmmyCTybBkyZIR/3xEJI78kkaU1GvgLJPgmkkX/uWHyJK4Esy6idoDtHjxYtTV1WHNmjVQKpVISkpCdna2qTG6vLwcUum5jFZdXY0pU6aYvn7xxRfx4osvYs6cOcjNzQUAVFZWYsmSJWhoaEBgYCBmzZqFffv2ITAwcEQ/GxGJo7KpHdlHjQsp0ieGwM9dLnJF5KhiAtxRUNbElWBWSvQm6JUrV2LlypX9vtYbanpFRUVBEC6+rfj7779vrtKIyMboDQL+9N/D0HUbMNrPDZfF+ItdEjkwjgBZN9GPwiAiMpeXvjqJH87Uw1kmwS1TR3HTQxJV715AZ+s5AmSNGICIyC58dqga/84tBgDcPGUUAjwvvFs80UjoPRW+pE5zydkLGnkMQERk834qbcQjHx4CANx7RQwSI3zELYgIQKSfG2RSCTQ6PVRqrdjl0K+I3gNERDQc+SWNWL41Hx1deswZG4hH08fhgwOVYpdFDujd/ecfu+Tj6owGjQ6bvz+LJ66fIEJVdCEcASIim7XnVB2Wb81Hu06PWXEBePXOZDjJ+NcaWY/AnqnYulaOAFkbjgARkc0RBAGb9pzFC7tOwCAAs8cEYPPSFLg4y8QujaiPAA8FgFbUtTEAWRsGIDvV2aXHP74+hdoWLZKjfLkahuxGTUsH/vLxEXxzohYAcNu0CDy1YCIUTgw/ZH0CPYwjQPUcAbI6DEB26r38cry65ywAoKC8CTdNCUewl4vIVRENncEg4J38cjz35Qm0abshk0hwfWIoEkb54L8FVWKXR9Sv3tWI9RwBsjoMQHZIEAS809OMJwFQ3tiOzd+fxSPzxkHBKQKyQWdq27D6o0P4qbQJgHF1DUM92YLeHqDm9i50duk5TWtFGIDs0L6zjThT2wY3uQwrZsdg295StHR0obhOgwlhPOiVbEeX3oBX9xTjlZwz0OkNcJPL8Gj6ODjJpJzWJZvgLpfBxVmKzi4DShs0iA/h38HWgssl7NDb+8oAAAt7fkOOD/EEAJyubRWzLKJBOVLVghv++QNe/OoUdHoDrhwXiN2Zc7D88miGH7IZEonE1AfEIzGsC0eA7EytuhO7eg6C/F3qaBRVNGNssCf2lzTidK3jbcfe374cv3R7auQIVUIDpTcI2LSnGOu/PoUuvQBfN2esvWEiFiSFQcLgQzYowEOBiqYOFDvg38HWjAHIznz0cxW6DQKSR/tiQpgXiiqaERPgDqkEaNTo0NCmhb8Hjwgg61TXqsX/e68Q+842AgDmTwzB326axP9myaYF9fQB8VR468IAZGcO9DSJXjc51PScwlmG0f7uKKnX4FRtG2bwhwkNwEiPnhWWN+H+twuhVHfCXS7DkzdOxK3JozjqQzYvqKdZ3xFH4a0Ze4DszJGqFgDA5FHefZ4fE2Q8lO+0in1AZH0+PViN217dB6W6E7GB7vhk5SwsSolg+CG70DsCdKa2DXoDD0W1FgxAdqSuVQuluhMSCTAhtO9Kg7HBxkbos/UadBsMYpRHdB7jjs7F+MN7P0OnN2DehGB8snIW4noCO5E98HWXw0kqgbbbgKqmDrHLoR4MQHbkSLVx9CcmwB3uir6zmyHeLnBXOEHXbUB5Q7sY5RH1IQgC/vb5cTz75QkAwF2XR2Pj75LhoeDMPNkXqURi2g+Iq3GtBwOQHTnaM/01Kdz7vNekEgmi/N0AANXN/A2ExKU3CPjzx4fx+g8lAIDHrxuPNTdMgEzKKS+yT+cCEPuArAUDkB05UqUGAEzuJwABMO2aq+KZNCQivUHAwx8exHv5FZBKgOdvScA9s2PELovIooI8exqhVQxA1oJjzXbkcM8I0MSw/gNQbyNerbpzxGoi+qVuvQGZHxzEpwer4SSVYP1tSbg+IUzssogs7lwjNKfArAUDkJ1o0uhQ1TO1NTG8/63We0eAalu1EASBK2xoWAa7TL5Lb8CDO4rw+aEaOEkl+NftUzF/UoglSySyGkFe56bA+PevdeAUmJ3obYCO8neDl4tzv9f4e8ghlQDabgNaOrpGsjxycF16A/7w3s/4/FANnGUSbPxdMsMPORR/dwWcZRK06/SobuEovDVgALITvf0/Ey/Q/wMATlIpAno2QVSp2QdEI0PXbcAD7xTiyyNKyGVSbPpdMq6eECx2WUQjSiaVIDrAHQD3Y7MWDEB2oncE6EIN0L2CTNNg/A2ELE/brcf97xTiq2MqyJ2keHVpMuaOZ/ghxzQmyLgf2xmuBLMK7AGyE8drjCNAv94A8deCPRU4Ao4AkeV1dhnDzzcnaqFwkuK1pSmYMzYQAA+pJcc0JtgDOMyVYNaCAcgOdOsNqGg0bm4YE+h+0Ws5AjRw/CE9dF16A2781w84pWqDs0yCO1JHo6qp45L/TInsWe8I0CmuBLMKnAKzA9XNnejSC5A7SRHm7XrRa4NNS+G1MAg8k4bMr0tvwNv7ykzhZ+mMKB5tQQRgbLDxz8EpZSsMPBNMdAxAdqCkQQMAGO3nBukldtL191BAJpFApzegpZ0rwci8dN0GvJVXhtO1bZDLpFg2MwqxgQw/RAAQFeAOuUwKjU5v2raExMMAZAdK640BKCrg4tNfgHElQu+W7CpOg5EZ6boN2J5XijN1bZA7GcNPTADDD1EvZ5nUNBp6QslpMLExANmBkp4AFD2AAASc25Crlo3QZCbabj3ezCvF2XoNFE5SZMyMGvB/j0SOJD7E2Ad0omfhComHTdB2oLRnCizKf4ABqLcPiGeCWZSjNFFru/TYlleKsoZ2Y/i5PBqRfm5il0Vklcb1BiDuBSQ6BiA7cG4KbGA/dPzdjQGoUcMARMPT2aXHtr2lKG9sh4uzFBkzoxHB8EN0QeM4AmQ1OAVm47r0BlQ0GZvpBjrl4OcuBwA0anQWq4vsX2eXHlt/LDGFn7suZ/ghupTxPXu1lTa0o7NLL3I1jo0jQDausqkDeoMAF2cpgj1dBvSe3gCk7uxGZ5ceLs4yS5ZIdqhDp8fWvSWobOqAq7MMd82KRrjPxbdgGAzuF0T2KshTAR83ZzS3d+FMbRsmXWL3frIc0UeANmzYgKioKLi4uCA1NRX5+fkXvPbo0aO45ZZbEBUVBYlEgvXr1w/7nrbONP3l737JJfC93OQyKJyM/+p7N1AkGqhfhh83uQx3mzn8ENkziUSCccE902BcCSYqUQPQjh07kJmZibVr16KwsBCJiYlIT09HbW1tv9e3t7cjJiYGzz77LEJC+j9JerD3tHUl9YNrgAaMfwD9e0aByhmAaBD6Cz9hDD9Eg9I7DXZSyT4gMYkagNatW4cVK1YgIyMDEyZMwKZNm+Dm5oYtW7b0e/20adPwwgsv4LbbboNCoTDLPW2daQXYIJcc+/YEoLIGBiAamP7CT+gldh4novOZGqE5AiQq0QKQTqdDQUEB0tLSzhUjlSItLQ15eXkjek+tVgu1Wt3nYSvO7QE0uOZTjgDRYDD8EJkPA5B1EC0A1dfXQ6/XIzg4uM/zwcHBUCqVI3rPrKwseHt7mx4RERFD+v5iGOweQL38epbCMwDRpXR2MfwQmVNvD1BdqxYNbdyORCxcBQZg9erVyMzMNH2tVqttIgTpug2oGuQS+F5+pikwjdnrooGxhY0SO7v02PLjudVeDD9Ew+eucEJ0gDtK6jU4Wq3GFWMDxS7JIYk2AhQQEACZTAaVStXneZVKdcEGZ0vdU6FQwMvLq8/DFlQ2tcMgAK7OMtP5XgPVG4Aqmjp4KjH1q0Nn3OenN/zcM5vhh8hcJoQZf84cqW4RuRLHJVoAksvlSE5ORk5Ojuk5g8GAnJwczJgxw2ruac16p68i/FwhkQxsCXwvb1dnSCXGUSQeikq/pmzpxGvfF6OCIz9EFjEpzLj/z9Fq2+k5tTeiToFlZmZi2bJlSElJwfTp07F+/XpoNBpkZGQAAJYuXYrw8HBkZWUBMDY5Hzt2zPT/q6qqUFRUBA8PD8TFxQ3onvakdwfooZy7JJNK4OMmR6NGh7KGdv5wI5Mzta1YtuUnqNRaeLo4YfnMKP73QWRmE3tGgI4xAIlG1AC0ePFi1NXVYc2aNVAqlUhKSkJ2drapibm8vBxS6blBqurqakyZMsX09YsvvogXX3wRc+bMQW5u7oDuaU8qTSNAQzt+wN/dGIDKG9txWYy/OUsjG7XrqBKZO4qg0enh7y7HXZdHm7ZMICLz6Q1AJfUatHZ2wdPFWeSKHI/oTdArV67EypUr+32tN9T0ioqKgiBcul/lYve0J6YpMN+hBaDeH2zl3AvI4XV26fGPr0/h1T1nAQCp0X6YOz4YHgrR/4ogshu/Xvjg7eqMlo4uvJJzBtEB7lax8MGR8G83G1bRZAwuQ5kCA87tBVTGpfBWaaRWif1U2ojH/nsIZ+uMKwLvujwaq6+Nx4cHKs1yfyLqX5i3C1o6ulDd3DHolbw0fAxANqx35GaoU2B+3AzRoR2ubMH6r08h54TxmJhATwX+tnAS5k0c2ipMIhqcUB9XHFe2oqalQ+xSHBIDkI1qae+CurMbgHEV2FCYAhD3AnIYlU3t+PqYCv8trMLhKuPyW6kE+G1KBFZfMx7ebuxDIBopvYcIVzdzJa4YGIBsVO/0V4CHHG7yof1r9HMzBqCm9i6oO7vgxSY8uyEIAqpbOnFSqcYJZStOKlvxc3lzn9E+J6kE1yeE4g9zxyAm0EPEaokcU6i3CwCgtrUTXXqDyNU4HgYgG1UxzBVgAKBwlsHfXY4GjQ7lDe2YFO5trvJoBOkNAkrq23CkSo0jVS04Ut2CY9Vq0wjhL8mkEiSO8sb1CWFYkBQGf4/BbaBJRObj7eoMN7kM7To9VGqOAo00BiAbNdwVYL0i/d2MAaiRAciWtGu7seOncuw5VYcfzzSgpaPrvGucpBLEBLpjXIgX4kM8MSHMC9Oi/Liyi8hKSCQShPu44nRtGyqb2Ac00vg3oY0a7gqwXqP93PBzeTPKuBTe6ukNAo7XqPFTaSOK69rwyxNM3OQyTAj1wsQwL0wM98bEMC/EBXlA4SQTr2AiuqRwX2MAqmIAGnEMQDaqvNH4h2WoDdC9egMUV4JZL422GwdKG7G/pBHNvxjpCfV2wfhQL4wN8kC4rxtkUuNxKN16AQcrWjAxjCN6RNaudxS/95daGjkMQDZquLtA94r0N+49Ud7IlWDWpqldh9yTtfi5vBndPcM9bnIZpkX5ITnSFwGDPACXiKzPKF/jL7F1rVruCD3CGIBskMEgmOaLh9sDNNrf+H5OgVmPlo4u5J6sxYHSJuh7dj4P83HBjJgAJIzyhrNMtDOMicjMPF2c4ePmjOb2LhyubMHMuACxS3IYDEA2SNXaCZ3eACepxLSMcqh6p8CqmzvQpTfwh6uI2rTdyD1Zi/ySRtOIT2ygO34TH4wofzdIJBKRKyQiSxjl64bm9hYUVTYzAI0gBiAb1LsDdJiPK5yGGViCPBVQOEmh7TagqqkDUdyOfcR16w3YW9yAb0/WQttt3Askyt8NaROCERPA/XmI7F2EryuOVLXgYEWz2KU4FAYgG1TRM/013BVggHEZZqSfG07XtqG8sZ0BaAQJgoAj1WpkH6lBU7uxuTnMxwXpE0MQF+jBER8iBzGqp5WhiAFoRDEA2SDTHkDDXAHWa7S/MQCJdShqu7YbB8qaEO7rilgH2ZFY2dKJTw9WobRnNM/LxQnzJoQgKdIHUgYfIocS7uMKqQRQqbVQtnQiZJitDTQwDEA2yFwrwHpF+vWsBBvhM8F03QbkHFfhhzP10HYbIAFw05RwpET5jWgdI0nbrcc3x2vxY3E9DALgLJNg9phAXDEmEHIn9l8ROSK5kxTBXi6oaelEUUUz5nvzQOKRwABkg8y1C3SvyJ6RpJHeC2jd7nMnkXsqnNCq7cZHP1eho0uP2WMCR7QWSxMEAUer1fj8cI1p1+YJoV64PiEUPj1nshGR4xrl63YuAE1iABoJDEA2yFy7QPca3bMX0Eguha9v0+LNvaUAgBsSw5Aa7Yevjqrw3ek6fHlEiSh/d7ONcImtUaPD/w5W46SqFQDg6+aMGxLDEB/iJXJlRGQtIv1c8VMpUFjWJHYpDoMByMZ0dumhUmsBmHEKzP/cbtCCIIxI8+3m786io0uPcB9XXBbtB4lEgvmTQqDu7EJRRTO+OVGLZTOjLF6HJekNAr4/XYdvTtSi2yBAJpFg9tgAXDk2iNNdRNRH7y+iRZXN0HbreYzNCGAAsjG9GyB6KJzg62aeHUNH+bpCIgHadXo0aHQIsPAJ4Q1tWmzPKwMAzB0f1Cdw/SY+CAcrmnFS1YpKG94a/khVC/6dewY1LcYTnmMC3HFjUhiCPEeuufHd/eUXff321MgRqoSILsXfXY4ADznq23Q4XNli172Q1oK/htqYip4+HWNoMc9IjcJJhlAv4w/mkZgG2/x9CTq69EgY5Y1xwZ59XgvwUCApwgcA8E1Pf5At6ezS49kvT2DBhh9R09IJV2cZbk0ehbtnRY9o+CEi2yKRSJAy2hh6firlNNhIYACyMebu/+l1bhrMsivBDAYBHxVWAgDuvzKu3xB31bggSACcULbiSFWLResxp/1nG3DNy99j055i6A0CJod7449Xj8XUSF/u6UNEl5QS5QsAOFDaKHIljoEByMb07gJt7gZh06nwDR1mve+vHapqQW2rFh4KJ1wV3/9KrwBPBSaPMp5kvj2v1KL1mENrZxf+8vFhLH5tH0rqNQj2UuC1O5OxZHokPBScZSaigZnWM+11oKwJhp7jcMhyGIBsjKVGgEwrwSw8ArT7mBIAMGds4EWb/C6L9gcA/O9gDdSdXRataTi+OaHCvH98h3d6+m2WTI/AV3+cg3kTuYyViAZnQpgXXJ1laOnowpm6NrHLsXsMQDamvLHnFHgz7QLdK8I0AmTZHqDdx1QAgKsnBF/0utH+bgj0VKCjS49Pfq6yaE1D0abtxqr3f8Zd2w6gpqUTo/3d8O6KVGTdnABvV/M0pxORY3GWSTEl0gcA8BOnwSyOAciGCIJg2gXa7CNAfueWwltKWYMGp1RtkEkluGpc0EWvlUgkmN4zHPzO/nIIgnUMBwuCgKKKZqz/+hQ+KaqGVALce0UMslddgZmxPMWZiIand/XXATZCWxwbFGxIc3sXWrXdAM4dnmcuo3uaoGtbtejQ6eEqN/8eFL2jP6nRfvAewBL+KZE+2H1chRPKVhysbDGtDhNLc7sOnxSd29AwPsQTz92SgESR6yIi+zGtpxF6/9mGEduXzVExANmQ3v6fIE8FXJzNG1C8XZ3h6eKE1s5ulDe2Y1yI56XfNEhfDXD6q5eb3AnXTQ7Fxz9X4b395aIFIIMg4KfSRmQfUULbbTCNYP37jqk2vaHhpfYJIqKRlzzaF84yCapbOlHe2G7qzyTzs92/vR1Qhan/x/xHREgkEtMokCWmwdSdXaalnWnjBxaAAGDJdONmfZ8erEarCM3Qta2deP37EnxSVA1ttwGRfm5YeVUcfhPP3ZyJyPzc5E6YEmEcBfrxTIPI1dg3/g1uQ8ot1P/Ta7Rf75lg5l8JdqC0EQYBiPJ3G1SAmxbli7ggD3R06bGzqNrsdV1IfZsWnxRV4ZWc0yht0EAuk+L6hFDce0UMgr24oSERWc7MOOMq2B+L60WuxL4xANmQc6fAm3cFWK8ICzZC7y8xjv5Mjx7c9u4SicQ0CvSuhZuhGzU67DlVh1Xv/4yZWd9gf4kxtI0P8cSquWMwMzYAUs7HE5GFXR5nXFCRV9zA/YAsiD1ANqS03jgyExVgmTlhS06B7T9rDECpPfv7DMbNU8LxXPYJHK9R41Bly7Cbjrv0BihbOqFSd+JMbRtOqtQ4qWxDfZu2z3WjfF0xf2IIYgI9hvX9iIgGI3GUD1ydZWjU6HBS1YrxoV5il2SXGIBsyNl648ZY0RYKQJEW2gtIo+3G4Z4jLQY7AgQAvu5yXDspBDuLqvFefvmQApAgCDhT24afK5pxrFoNnd7Q73URfq6YFReI26dHmmomIhpJcicppkf7Yc+pOvx4pp4ByEIYgGyERtsNldo4QhETYJkRid4AVNnUAb1BgExqnumewvIm6A0Cwn1ch9zAvWR6JHYWVePjn6uwKm0MQr0HPg3YqNHh04NVOKU6t7Oqu8IJoV4uuGJsIOJDPDE2xBNjgjzg/oujKxiAiEgsl8f5Y8+pOuwtbsA9s2PELscuWUUP0IYNGxAVFQUXFxekpqYiPz//otd/+OGHiI+Ph4uLCyZPnowvvviiz+vLly+HRCLp85g/f74lP4LFlfRMf/m7ywe0h85QhPm4wkkqgU5vgFLdabb7npv+GvzoT6/p0X6YFuULbbcBr+ScHvD7Csqa8HLOKZxStcFJKsFlMX64b04s/nxNPO6aFY01N0zAb6dFICnCp0/4ISISU+/GqvvPNqDrAiPWNDyiB6AdO3YgMzMTa9euRWFhIRITE5Geno7a2tp+r9+7dy+WLFmCu+++Gz///DMWLlyIhQsX4siRI32umz9/PmpqakyP9957byQ+jsWc7QlAlpr+AgCZVIJRPQ3W5lwJlj/EBuhfkkgk+NM18QCADw5UovgS5+QYDAKeyz6B/xZWoksvIDrAHX/4zRjcmBiOCD83bi5GRFZtQqgXfN2codHp8XN5s9jl2CXRA9C6deuwYsUKZGRkYMKECdi0aRPc3NywZcuWfq9/+eWXMX/+fDzyyCMYP348nnnmGUydOhX/+te/+lynUCgQEhJievj6+o7Ex7GYkjpjIIkJtOymWJG9h6KaqQ+os0uPoopmAEBqzOAboH8pebQf0sYHQ28Q8OKukxf9nivfK8TG3GIAwFXjgnD3rGgEeCqG9f2JiCzp3f3lpsf7P1WYNkH81zdnuHGpBYgagHQ6HQoKCpCWlmZ6TiqVIi0tDXl5ef2+Jy8vr8/1AJCenn7e9bm5uQgKCsK4ceNw3333oaHhwhtKabVaqNXqPg9rU2JqgLbsiqTYnoB1WmWek4iLKpqh0xsQ6KlAlP/w9y96dP44SCXAl0eUeHtf2Xmv17Vqsfi1ffjisBLOMgkWJY/C1ROCuXydiGzO2GDjjvwnVdb3M8keiBqA6uvrodfrERzcd2fg4OBgKJXKft+jVCovef38+fOxfft25OTk4LnnnsOePXtwzTXXQK/X93vPrKwseHt7mx4RERHD/GTmNxJTYAAwrucP3OnaVrPc75f9P+aYdhob7In7rowFADy+8wje6glBeoOADw5U4Pp/fo+DFc3wcXPG23enYkqkbY/8EZHjGhvsAQkAlVqL5nad2OXYHbvs+rzttttM/3/y5MlISEhAbGwscnNzMXfu3POuX716NTIzM01fq9VqqwpBgiCM2BTYmJ4AdEplngCUX2oceRtOA/SvPTxvHHTdBmz+vgRP7DyC5788ARe5DHWtvavk3PHG8mmIDnBHcZ35d7UmIhoJbnInRPq5oayxHSeU5vk7mc4RNQAFBARAJpNBpVL1eV6lUiEkJKTf94SEhAzqegCIiYlBQEAAzpw5028AUigUUCistz+kvk2HVm03JJJzmxVayphg4xSbSq1FS3vXsFac6boNKChrAjD8/p9fkkgk+PO14yF3kmLTnrNo1XajVdsNb1dnrLwqDnfOGG32w2KJiMQwLsQTZY3tOMkAZHaiToHJ5XIkJycjJyfH9JzBYEBOTg5mzJjR73tmzJjR53oA2L179wWvB4DKyko0NDQgNDTUPIWPsN4l8KN8XaFwsuwPdi8XZ4R5G8+6OjXMabDDVS3o7DLAz12OMUHm7V2SSCR4JD0eh5+ch68zr8B7Ky7D949dhRVXxDD8EJHdGBdiHJU/W9+Gzq7+2zhoaERfBZaZmYnNmzfjzTffxPHjx3HfffdBo9EgIyMDALB06VKsXr3adP2qVauQnZ2Nl156CSdOnMCTTz6JAwcOYOXKlQCAtrY2PPLII9i3bx9KS0uRk5ODBQsWIC4uDunp6aJ8xuE6WzcyDdC9zDUNtr/EOP01LcrXYsvO3eROiAvyxIxYf3i5WGZ/JCIisYR4ucDb1RldegF5xTwd3pxED0CLFy/Giy++iDVr1iApKQlFRUXIzs42NTqXl5ejpqbGdP3MmTPx7rvv4rXXXkNiYiL+85//YOfOnZg0aRIAQCaT4dChQ7jxxhsxduxY3H333UhOTsb3339v1dNcF9M7AhRj4QboXmN7psFODXPItXf/n6Gc/0VERMbR7t5RoOwj/S8OoqGxiibolStXmkZwfi03N/e85xYtWoRFixb1e72rqyt27dplzvJE17sCzNIN0L3GmkaAhr4UvltvwIFSY//PcDZAJCJydJPCvJFf0ohdx5T4q34SnGWij13YBf5TtAHnpsBGNgANZyn88ZpWtGm74enixIP8iIiGITrAHW5yGZrbu7DvLKfBzIUByMp1dulNU2C9wcTS4noaluvbdGho0w7pHuf6f/zMdqgqEZEjkkklmBjmDQD44nDNJa6mgWIAsnKnVK0wCICfuxxBI3SUg7vCyXQm2FCnwfYWm3//HyIiRzU53BiAdh1VoZuHo5oFA5CVO1Zt3AJ9QqjXiB7gOZwdobv0BuzvGaa9PC7ArHURETmi6AB3+LnL0ajRYV/PDvs0PAxAVu54jTEAjQ8dmemvXr1L4Yey+dahymZodHr4uDljAvt/iIiGTSaVIH2iccPfzw5Vi1yNfWAAsnLHegLQhLCRDRK93+9QZcug37v3jHH0Z2asP6Ts/yEiMosbEo2b+X5+qAYdOm6KOFwMQFbMYBBwvMY4AjPSK6mSRxsPET1Wo4ZG2z2o9/5YXA8AmBnL6S8iInO5LNofEX6uaNV2I/som6GHiwHIilU2daBN2w25TIrYwJHZBbpXuI8rQr1doDcIOFjRPOD3dej0KCwzXs/+HyIi85FKJViUbDyo+4OfKkWuxvYxAFmx3umvMcEeomx81TsKdKDnQNOBOFDWCJ3egDBvF0RZ+OBWIiJHc0vyKEgkQN7ZBpQ3tItdjk1jALJi5xqgxWkknhZlXMI+mAD0Y2//T1zAiK5aIyJyBOE+rpjVM7r+n4IKkauxbQxAVszUAC1SAOodASosa4LeIAzoPd+frgMAXB7H87+IiCzhtyk902AHKtHFPYGGjAHIiok9AhQf4gl3uQxt2u4BLYcvb2jH0Wo1pBLgijGBI1AhEZHjmTcxGAEecijVndwZehgYgKxUS0cXKps6AIg3AuQkk2JKpHEUqKDs0htvfXHE+AdxRqw//D1GZtdqIiJHo3CSYemMKADAGz+UQBAGNkJPfTEAWanCcmPfTaSfG7zdnEWrYzCN0L2/iVw7OdSiNRERObo7UiOhcJLiUGUL8ku4M/RQMABZqd4Tf8U+SyslyhiA8ksaYbhIH1BFYzsOVbZAKoFpt1IiIrIMfw8FbkkeBQDY/H2JyNXYJgYgK7W/56yXy2LEbSZOGe0HD4UTalo6kV964d8yekd/UqP9EcDpLyIii7t7VjQAIOeECqdVgz+2yNExAFmhNm03DlcZj6BIjRF3BMhVLsP1CcYprQ8PXHjjLdP0VwKnv4iIRkJsoAfSJwZDEICXvjoldjk2hwHIChX0LDsf5euKUb7ibya4KMU4zPrF4Rq09XMsxtHqFhzsmf6az+kvIqIR89C8cZBKgOyjShQNYtd+YgCySr39P2JPf/WaGumLmEB3dHTp8cWh85dcvrDrJADg+oQwBHpy+ouIaKSMDfbEzVONv6Q+9+UJrggbBAYgK2RtAUgikeDWnma7D3+18+j+sw3IPVkHJ6kEmVePFaM8IiKH9mDaGMhlUuSdbcCeU3Vil2MzGICsjEbbjcOVPf0/Iq8A+6Vbpo6CVAL8VNqE7CNKAIAgCHi+Z/Rn8bQIRAW4i1kiEZFDGuXrhjtnjAYArP30KDq79CJXZBsYgKxMQVkTug0Cwn1cEeEnfv9Pr2AvF9w2PRIAsPLdQmz7sQS/f6sABWVNcHGW4g9zx4hcIRGR43owbQxCvFxQ1tCO9V+fFrscm8AAZGWyjxpHV6zxLK2nb5yIhUlh6DYIePJ/x/DVMRWkEuCx+fEI9nIRuzwiIofl6eKMZxZOAgBs/v4sjla3iFyR9XMSuwA6R9utx+c9TcYLksJFruZ8TjIpXvptEuROUnxwoBLzJgTjkfRxGBPsKXZpRER279395Rd9/fbUSFw7OQRfHFbioQ8O4uP7L4erXDZC1dkejgBZkdyTdWjp6EKwl8JqGqB/TSaV4PlbE3HkqXS8tjSF4YeIyIo8eeNEBHgocELZijWfHBG7HKvGESAr8nFhFQBgYVI4ZFKJyNVcnIeC/+kQEVmT3hGiBUlh2PJDCT4sqITeICAlyrig5vbUSDHLszocAbISLe1d+OZELQBg4RTrm/4iIiLbEBvogasnBAMAPjlYjeK6NpErsk4MQFbiiyM10OkNiA/xxPhQL7HLISIiG3bF2EBMDPOC3iDgrX1lqGxqF7skq8MAZAW69Aa8/v1ZAMBNHP0hIqJhkkokWJwSgZhAd+i6Ddi2txRHqrgy7JcYgKzAe/nlKK7TwM9djiWcoyUiIjNwkklxZ+pohPu4ol2nx+JX8/DD6Xqxy7IaDEAia+noMm1a9cerx8LLxVnkioiIyF4onGW4e1Y0YgLcodHpkbEtH2/vK+OZYWAAEt2Gb8+gUaNDXJAHlkyLELscIiKyMy7OMiyfGYXrE0LRpRfw+M4jeODdQrR0dIldmqgYgET0SVEVNvf0/vz52ng4yfivg4iIzM9JJsUrt00x/qyRSvDFYSXS1u3BfwsqYTA45miQVfzE3bBhA6KiouDi4oLU1FTk5+df9PoPP/wQ8fHxcHFxweTJk/HFF1/0eV0QBKxZswahoaFwdXVFWloaTp+2rrNRvj6mQuYHByEIwLIZo3HVuCCxSyIiIjsmlUpw7xWx+M99MxET4I66Vi0e+vAgbtq4F7uOKh0uCIkegHbs2IHMzEysXbsWhYWFSExMRHp6Ompra/u9fu/evViyZAnuvvtu/Pzzz1i4cCEWLlyII0fO7Xj5/PPP45VXXsGmTZuwf/9+uLu7Iz09HZ2dnSP1sS6oQ6fHi7tO4r53CqA3CLh5SjjW3jAREol1b3xIRET2ISnCB18+OBuPzY+Hm1yGgxXN+P1bBUhbtwf/+uY0yhscY8m8RBC5Eyo1NRXTpk3Dv/71LwCAwWBAREQE/t//+3/405/+dN71ixcvhkajwWeffWZ67rLLLkNSUhI2bdoEQRAQFhaGhx56CA8//DAAoKWlBcHBwdi2bRtuu+22S9akVqvh7e2NlpYWeHmZb0+er44q8dT/jqGquQMAcH1CKNYvTrLo1NdAzo4Rk6XrG879h1vbpd5PRDSS+vs7q7a1E1t/LMXb+8rQ2tltej4m0B0zYvyRFOGDcSGeiA5wh4fCyep/WR/Mz29RzzPQ6XQoKCjA6tWrTc9JpVKkpaUhLy+v3/fk5eUhMzOzz3Pp6enYuXMnAKCkpARKpRJpaWmm1729vZGamoq8vLwBBSBLOVajRlVzB8J9XPHE9ROQPjHY6v9jIiIi+xXk6YLH5sfj/itj8eURJT4tqsbe4nqcrdPgbJ0G7/ziFzlXZxkCPOUI9FAgwEMBN7kMzjIp5E49j57/7yyTQgKg98fbL3/OSSSABMavp0b6IFXEcy9FDUD19fXQ6/UIDg7u83xwcDBOnDjR73uUSmW/1yuVStPrvc9d6Jpf02q10Gq1pq9bWoybRanV6kF8mku7fUogBF07bp8+Gq5yGVpbW816//60ay7+Pcz9GQfL0vUN5/7Dre1S7yciGkmvf3P0oq//NiUC88d6o6W9CwfKGnGgrAkna1pxurYVTe1d0GgBTRtQZqZ67pkVjfEB5t36pffv5YFMbvFESwBZWVl46qmnzns+IsIyy9IzL33JiFkhdgGXYOn6hnN/a/9nR0Q0GCP9d9ra9cBaC927tbUV3t7eF71G1AAUEBAAmUwGlUrV53mVSoWQkJB+3xMSEnLR63v/V6VSITQ0tM81SUlJ/d5z9erVfabVDAYDGhsb4e/vb9NTVGq1GhEREaioqDBrL5O14ue1b/y89s2RPq8jfVZgZD+vIAhobW1FWFjYJa8VNQDJ5XIkJycjJycHCxcuBGAMHzk5OVi5cmW/75kxYwZycnLw4IMPmp7bvXs3ZsyYAQCIjo5GSEgIcnJyTIFHrVZj//79uO+++/q9p0KhgEKh6POcj4/PsD6bNfHy8nKIP2S9+HntGz+vfXOkz+tInxUYuc97qZGfXqJPgWVmZmLZsmVISUnB9OnTsX79emg0GmRkZAAAli5divDwcGRlZQEAVq1ahTlz5uCll17Cddddh/fffx8HDhzAa6+9BsDYbPXggw/ir3/9K8aMGYPo6Gg88cQTCAsLM4UsIiIicmyiB6DFixejrq4Oa9asgVKpRFJSErKzs01NzOXl5ZBKzy0TnzlzJt599108/vjj+POf/4wxY8Zg586dmDRpkumaRx99FBqNBvfeey+am5sxa9YsZGdnw8XFZcQ/HxEREVkf0QMQAKxcufKCU165ubnnPbdo0SIsWrTogveTSCR4+umn8fTTT5urRJukUCiwdu3a86b37BU/r33j57VvjvR5HemzAtb7eUXfCJGIiIhopIl+FAYRERHRSGMAIiIiIofDAEREREQOhwHIjm3YsAFRUVFwcXFBamoq8vPzxS7JIr777jvccMMNCAsLg0QiMZ0LZ6+ysrIwbdo0eHp6IigoCAsXLsTJkyfFLstiNm7ciISEBNMeIjNmzMCXX34pdlkj4tlnnzVt7WGPnnzySUgkkj6P+Ph4scuyqKqqKvzud7+Dv78/XF1dMXnyZBw4cEDssiwiKirqvH+/EokEDzzwgNilAWAAsls7duxAZmYm1q5di8LCQiQmJiI9PR21tbVil2Z2Go0GiYmJ2LBhg9iljIg9e/bggQcewL59+7B79250dXVh3rx50Gg0YpdmEaNGjcKzzz6LgoICHDhwAL/5zW+wYMECHD168XONbN1PP/2EV199FQkJCWKXYlETJ05ETU2N6fHDDz+IXZLFNDU14fLLL4ezszO+/PJLHDt2DC+99BJ8fX3FLs0ifvrppz7/bnfv3g0AF13FPaIEskvTp08XHnjgAdPXer1eCAsLE7KyskSsyvIACB9//LHYZYyo2tpaAYCwZ88esUsZMb6+vsLrr78udhkW09raKowZM0bYvXu3MGfOHGHVqlVil2QRa9euFRITE8UuY8Q89thjwqxZs8QuQzSrVq0SYmNjBYPBIHYpgiAIAkeA7JBOp0NBQQHS0tJMz0mlUqSlpSEvL0/EysgSWlpaAAB+fn4iV2J5er0e77//PjQajen4G3v0wAMP4LrrruvzZ9henT59GmFhYYiJicEdd9yB8vJysUuymE8//RQpKSlYtGgRgoKCMGXKFGzevFnsskaETqfD22+/jbvuustqzthkALJD9fX10Ov1pt20ewUHB0OpVIpUFVmCwWDAgw8+iMsvv7zPbuj25vDhw/Dw8IBCocD//d//4eOPP8aECRPELssi3n//fRQWFpqO/7Fnqamp2LZtG7Kzs7Fx40aUlJRg9uzZaG1tFbs0izh79iw2btyIMWPGYNeuXbjvvvvwhz/8AW+++abYpVnczp070dzcjOXLl4tdiolV7ARNREPzwAMP4MiRI3bdNwEA48aNQ1FREVpaWvCf//wHy5Ytw549e+wuBFVUVGDVqlXYvXu3Qxzdc80115j+f0JCAlJTUzF69Gh88MEHuPvuu0WszDIMBgNSUlLw97//HQAwZcoUHDlyBJs2bcKyZctErs6y3njjDVxzzTUDOqV9pHAEyA4FBARAJpNBpVL1eV6lUiEkJESkqsjcVq5cic8++wzffvstRo0aJXY5FiWXyxEXF4fk5GRkZWUhMTERL7/8sthlmV1BQQFqa2sxdepUODk5wcnJCXv27MErr7wCJycn6PV6sUu0KB8fH4wdOxZnzpwRuxSLCA0NPS+0jx8/3q6n/QCgrKwMX3/9Ne655x6xS+mDAcgOyeVyJCcnIycnx/ScwWBATk6OXfdNOApBELBy5Up8/PHH+OabbxAdHS12SSPOYDBAq9WKXYbZzZ07F4cPH0ZRUZHpkZKSgjvuuANFRUWQyWRil2hRbW1tKC4uRmhoqNilWMTll19+3pYVp06dwujRo0WqaGRs3boVQUFBuO6668QupQ9OgdmpzMxMLFu2DCkpKZg+fTrWr18PjUaDjIwMsUszu7a2tj6/MZaUlKCoqAh+fn6IjIwUsTLLeOCBB/Duu+/ik08+gaenp6mvy9vbG66uriJXZ36rV6/GNddcg8jISLS2tuLdd99Fbm4udu3aJXZpZufp6XleL5e7uzv8/f3tssfr4Ycfxg033IDRo0ejuroaa9euhUwmw5IlS8QuzSL++Mc/YubMmfj73/+O3/72t8jPz8drr72G1157TezSLMZgMGDr1q1YtmwZnJysLHKIvQyNLOef//ynEBkZKcjlcmH69OnCvn37xC7JIr799lsBwHmPZcuWiV2aRfT3WQEIW7duFbs0i7jrrruE0aNHC3K5XAgMDBTmzp0rfPXVV2KXNWLseRn84sWLhdDQUEEulwvh4eHC4sWLhTNnzohdlkX973//EyZNmiQoFAohPj5eeO2118QuyaJ27dolABBOnjwpdinn4WnwRERE5HDYA0REREQOhwGIiIiIHA4DEBERETkcBiAiIiJyOAxARERE5HAYgIiIiMjhMAARERGRw2EAIiIiIofDAEREI+rKK6/Egw8+aNXfY9u2bfDx8TFbPURkfRiAiMihRUVFYf369X2eW7x4MU6dOiVOQUQ0IqzsZDIiIvG5urra5cGyRHQOR4CISDRarRYPP/wwwsPD4e7ujtTUVOTm5ppeb2howJIlSxAeHg43NzdMnjwZ7733Xp97aDQaLF26FB4eHggNDcVLL7004O9/5ZVXoqysDH/84x8hkUggkUgAnD8F9uSTTyIpKQlbtmxBZGQkPDw8cP/990Ov1+P5559HSEgIgoKC8Le//a3P/Zubm3HPPfcgMDAQXl5e+M1vfoODBw8O/h8UEZkdAxARiWblypXIy8vD+++/j0OHDmHRokWYP38+Tp8+DQDo7OxEcnIyPv/8cxw5cgT33nsv7rzzTuTn55vu8cgjj2DPnj345JNP8NVXXyE3NxeFhYUD+v4fffQRRo0ahaeffho1NTWoqam54LXFxcX48ssvkZ2djffeew9vvPEGrrvuOlRWVmLPnj147rnn8Pjjj2P//v2m9yxatAi1tbX48ssvUVBQgKlTp2Lu3LlobGwc4j8xIjIbsY+jJyLHMmfOHGHVqlVCWVmZIJPJhKqqqj6vz507V1i9evUF33/dddcJDz30kCAIgtDa2irI5XLhgw8+ML3e0NAguLq6CqtWrRpQPaNHjxb+8Y9/9Hlu69atgre3t+nrtWvXCm5uboJarTY9l56eLkRFRQl6vd703Lhx44SsrCxBEATh+++/F7y8vITOzs4+946NjRVeffXVAdVGRJbDHiAiEsXhw4eh1+sxduzYPs9rtVr4+/sDAPR6Pf7+97/jgw8+QFVVFXQ6HbRaLdzc3AAYR2V0Oh1SU1NN7/fz88O4cePMXm9UVBQ8PT1NXwcHB0Mmk0EqlfZ5rra2FgBw8OBBtLW1mT5Lr46ODhQXF5u9PiIaHAYgIhJFW1sbZDIZCgoKIJPJ+rzm4eEBAHjhhRfw8ssvY/369Zg8eTLc3d3x4IMPQqfTjXi9zs7Ofb6WSCT9PmcwGAAYP19oaGifnqZeXGJPJD4GICISxZQpU6DX61FbW4vZs2f3e82PP/6IBQsW4He/+x0AwGAw4NSpU5gwYQIAIDY2Fs7Ozti/fz8iIyMBAE1NTTh16hTmzJkzoDrkcjn0er0ZPlFfU6dOhVKphJOTE6Kiosx+fyIaHjZBE5Eoxo4dizvuuANLly7FRx99hJKSEuTn5yMrKwuff/45AGDMmDHYvXs39u7di+PHj+P3v/89VCqV6R4eHh64++678cgjj+Cbb77BkSNHsHz58j7TUpcSFRWF7777DlVVVaivrzfb50tLS8OMGTOwcOFCfPXVVygtLcXevXvxl7/8BQcOHDDb9yGioWEAIiLRbN26FUuXLsVDDz2EcePGYeHChfjpp59MozmPP/44pk6divT0dFx55ZUICQnBwoUL+9zjhRdewOzZs3HDDTcgLS0Ns2bNQnJy8oBrePrpp1FaWorY2FgEBgaa7bNJJBJ88cUXuOKKK5CRkYGxY8fitttuQ1lZGYKDg832fYhoaCSCIAhiF0FEREQ0kjgCRERERA6HAYiI7Nb3338PDw+PCz6IyHFxCoyI7FZHRweqqqou+HpcXNwIVkNE1oQBiIiIiBwOp8CIiIjI4TAAERERkcNhACIiIiKHwwBEREREDocBiIiIiBwOAxARERE5HAYgIiIicjgMQERERORw/j9lW5UD3trNrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dataframe['lead_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19d5d588",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "082b6137",
   "metadata": {},
   "outputs": [],
   "source": [
    "## adr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "6a48c7b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='adr', ylabel='Density'>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkgAAAGwCAYAAABSN5pGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/P9b71AAAACXBIWXMAAA9hAAAPYQGoP6dpAABBL0lEQVR4nO3de3TU9Z3/8ddMLpNwSbhphmCAWFNRuUSDxFBcas02KrZGbRfRo8hS1K56sPFSQAzd1v5itVBEqdRaZdkt4tK1tKWYlQ1e2poGCaCilqICQXACFJNAgNzm8/sjzDeZkOvkO5n54vNxTg7mO5/55jPfcOR13p+byxhjBAAAAIs70h0AAACINgQkAACANghIAAAAbRCQAAAA2iAgAQAAtEFAAgAAaIOABAAA0EZspDvgVH6/XwcOHNDAgQPlcrki3R0AANANxhgdPXpUqampcrs7rhMRkEJ04MABpaWlRbobAAAgBPv27dM555zT4esEpBANHDhQUvMDTkpKinBvAABAd9TU1CgtLc36d7wjBKQQBYbVkpKSCEgAADhMV9NjmKQNAADQBgEJAACgDQISAABAGwQkAACANghIAAAAbRCQAAAA2iAgAQAAtEFAAgAAaIOABAAA0AYBCQAAoA0CEgAAQBsEJAAAgDYISAAAAG0QkBzoZEOTtlV8Lr/fRLorAACckQhIDvTE/+7U9T9/S//7vi/SXQEA4IxEQHKg3YdrJUn7q05EuCcAAJyZCEgOdOxkoySpvskf4Z4AAHBmIiA50NG65oDU2MQcJAAAwoGA5EDH6hokSQ1UkAAACAsCkgMdPTXE1kAFCQCAsCAgOYwxxpqD1EgFCQCAsCAgOUxdo1+Np/Y/YogNAIDwICA5TGB4TZIa2CgSAICwICA5zNGTDdZ/NzRSQQIAIBwISA5zrK6lgtRIBQkAgLAgIDnMsVZDbGwUCQBAeBCQHOZo6woSAQkAgLCIeEBavny5Ro8erYSEBGVnZ2vz5s2dtl+7dq3GjBmjhIQEjRs3Ths2bAh6/eWXX9bXv/51DR06VC6XS9u3bz/tHidPntTdd9+toUOHasCAAbrxxhtVWVlp58cKm6BJ2uyDBABAWEQ0IL300ksqKCjQokWLtHXrVk2YMEF5eXk6ePBgu+3feustzZgxQ7Nnz9a2bduUn5+v/Px87dixw2pTW1urKVOm6Cc/+UmHP/d73/ue/vCHP2jt2rV64403dODAAd1www22f75wONZ6kjYVJAAAwsJljIlYGSI7O1uXXnqpnn76aUmS3+9XWlqa7r33Xs2bN++09tOnT1dtba3Wr19vXbvsssuUmZmpFStWBLXds2eP0tPTtW3bNmVmZlrXq6urddZZZ2n16tX61re+JUn629/+pgsuuEClpaW67LLL2u1rXV2d6urqrO9ramqUlpam6upqJSUlhfwMeurpTbv001f/Lkm67NwhWnNHTp/9bAAAnK6mpkbJycld/vsdsQpSfX29ysvLlZub29IZt1u5ubkqLS1t9z2lpaVB7SUpLy+vw/btKS8vV0NDQ9B9xowZo5EjR3Z6n6KiIiUnJ1tfaWlp3f6Zdgqeg8QQGwAA4RCxgHT48GE1NTUpJSUl6HpKSop8Pl+77/H5fD1q39E94uPjNWjQoB7dZ/78+aqurra+9u3b1+2faSc2igQAIPxiI90Bp/B4PPJ4PJHuRtAyfzaKBAAgPCJWQRo2bJhiYmJOWz1WWVkpr9fb7nu8Xm+P2nd0j/r6elVVVfXqPpESvFEkAQkAgHCIWECKj49XVlaWSkpKrGt+v18lJSXKyWl/4nFOTk5Qe0nauHFjh+3bk5WVpbi4uKD77Ny5UxUVFT26T6QcY5k/AABhF9EhtoKCAs2cOVMTJ07UpEmTtHTpUtXW1mrWrFmSpNtuu00jRoxQUVGRJGnu3LmaOnWqFi9erGnTpmnNmjXasmWLnn32WeueR44cUUVFhQ4cOCCpOfxIzZUjr9er5ORkzZ49WwUFBRoyZIiSkpJ07733Kicnp8MVbNGkhmX+AACEXUQD0vTp03Xo0CEVFhbK5/MpMzNTxcXF1kTsiooKud0tRa7Jkydr9erVWrhwoRYsWKCMjAytW7dOY8eOtdr8/ve/twKWJN10002SpEWLFukHP/iBJOlnP/uZ3G63brzxRtXV1SkvL08///nP++AT917rITYCEgAA4RHRfZCcrLv7KNgt84evqup4cxVpaP94lT/yz332swEAcLqo3wcJPWeM4bBaAAD6AAHJQU42+NXYau8jNooEACA8CEgOcrSuIeh75iABABAeBCQHaT28JkmNfiOmkAEAYD8CkoMEVrD1j4+xrrEXEgAA9iMgOUjgHLZB/eKta+ymDQCA/QhIDhIISEP6twQkKkgAANiPgOQggSG2wUEBiQoSAAB2IyA5yLFTx4wMTIhVrNsliaX+AACEAwHJQQJDbAM9sYqNaQ5IVJAAALAfAclBAkNsAzyxiotp/tURkAAAsB8ByUGOBgJSQuuAxBAbAAB2IyA5SGCjyIEJcYpjiA0AgLAhIDnI0cAkbU+sYt0MsQEAEC4EJAc5FjTEdmoVm58hNgAA7EZAcpDAKragSdqNVJAAALAbAclBWleQYgMBiQoSAAC2IyA5SG2rZf7xgSE25iABAGA7ApKDBOYbxbhdLRUkAhIAALYjIDmIOTWa5na5Wi3zZ4gNAAC7EZAcpClQQXK52EkbAIAwIiA5iP9UCcnlkhWQOKwWAAD7EZAcJBCQYtwuxbqbh9jqqSABAGA7ApKDBFb0x7hdiosNVJAISAAA2I2A5CCBOUgulxTnZpI2AADhQkByCGNaglDQJG0/FSQAAOxGQHKIplY7ZrtdrfZBaqSCBACA3QhIDtH6RBG329XqsFoqSAAA2I2A5BB+07qC1LLMn1VsAADYj4DkEK0DUvNRI4Gz2BhiAwDAbgQkh2g7Byk+hmX+AACECwHJIVpPNXK7XIp1B4bYqCABAGA3ApJDnDYHKTYwxEYFCQAAuxGQHKKpzRykODeH1QIAEC4EJIdofVCty9WyzL/BzxAbAAB2IyA5RGAOktvVHIxaNoqkggQAgN0ISA4RqCDFnApI1io2KkgAANiOgOQQrQ+qlWTtg8QcJAAA7EdAcojAHO0Yd5shNgISAAC2IyA5RGAVm9saYgtUkBhiAwDAbgQkh/BbAan5+8BGkeyDBACA/QhIDuE/NQfJfSohxcUGhtioIAEAYDcCkkMEFqsFVrHFuZmkDQBAuBCQHKJlFVtwBYll/gAA2I+A5BDWPkinfmOxpypI9WwUCQCA7QhIDtF2o8g4a6NIAhIAAHYjIDnEaUNsMUzSBgAgXAhIDuFvs1FkHDtpAwAQNgQkh2i7D1IcO2kDABA2BCSHaLsPUuAstkaG2AAAsB0BySHaHjXSMknbyBhCEgAAdiIgOYQ5baPIll8dE7UBALAXAckhWlaxNX8fF+uyXmMeEgAA9iIgOUTLRpGn5iC1qiAxDwkAAHsRkBzCf9ocpFYVJDaLBADAVgQkhwiMogVWsblcLuu4EYbYAACwV8QD0vLlyzV69GglJCQoOztbmzdv7rT92rVrNWbMGCUkJGjcuHHasGFD0OvGGBUWFmr48OFKTExUbm6udu3aFdTm73//u6677joNGzZMSUlJmjJlil577TXbP5udWo4aablmrWRjiA0AAFtFNCC99NJLKigo0KJFi7R161ZNmDBBeXl5OnjwYLvt33rrLc2YMUOzZ8/Wtm3blJ+fr/z8fO3YscNq8/jjj2vZsmVasWKFysrK1L9/f+Xl5enkyZNWm2uvvVaNjY3atGmTysvLNWHCBF177bXy+Xxh/8yhsvZBcrUkpMBeSPVUkAAAsFVEA9KSJUs0Z84czZo1SxdeeKFWrFihfv366fnnn2+3/ZNPPqmrrrpKDz74oC644AL96Ec/0iWXXKKnn35aUnP1aOnSpVq4cKGuu+46jR8/XqtWrdKBAwe0bt06SdLhw4e1a9cuzZs3T+PHj1dGRoYee+wxHT9+PChotVVXV6eampqgr74UOGokMMQmSfFUkAAACIuIBaT6+nqVl5crNze3pTNut3Jzc1VaWtrue0pLS4PaS1JeXp7Vfvfu3fL5fEFtkpOTlZ2dbbUZOnSozj//fK1atUq1tbVqbGzUL37xC5199tnKysrqsL9FRUVKTk62vtLS0kL+7KFoanPUiNRSQWIOEgAA9opYQDp8+LCampqUkpISdD0lJaXDoS6fz9dp+8CfnbVxuVz6v//7P23btk0DBw5UQkKClixZouLiYg0ePLjD/s6fP1/V1dXW1759+3r2gXvJtFnmL7Us9ScgAQBgr9hId6CvGWN099136+yzz9af/vQnJSYm6rnnntM3vvENvf322xo+fHi77/N4PPJ4PH3c2xZN7cxBio8NBCSG2AAAsFPEKkjDhg1TTEyMKisrg65XVlbK6/W2+x6v19tp+8CfnbXZtGmT1q9frzVr1ugrX/mKLrnkEv385z9XYmKi/uM//sOWzxYO1hyk1pO03YEDa6kgAQBgp4gFpPj4eGVlZamkpMS65vf7VVJSopycnHbfk5OTE9RekjZu3Gi1T09Pl9frDWpTU1OjsrIyq83x48clNc93as3tdssfxRsutqxia7kWWObPKjYAAOwV0SG2goICzZw5UxMnTtSkSZO0dOlS1dbWatasWZKk2267TSNGjFBRUZEkae7cuZo6daoWL16sadOmac2aNdqyZYueffZZSc3zi+677z49+uijysjIUHp6uh555BGlpqYqPz9fUnPIGjx4sGbOnKnCwkIlJibql7/8pXbv3q1p06ZF5Dl0R9ujRqSW3bRZxQYAgL0iGpCmT5+uQ4cOqbCwUD6fT5mZmSouLrYmWVdUVARVeiZPnqzVq1dr4cKFWrBggTIyMrRu3TqNHTvWavPQQw+ptrZWd9xxh6qqqjRlyhQVFxcrISFBUvPQXnFxsR5++GF97WtfU0NDgy666CL97ne/04QJE/r2AfRAYBWby9U6IJ1a5h/FlS8AAJzIZQLLo9AjNTU1Sk5OVnV1tZKSksL+8/7zr3v1yLoduuoir1bc2rwdwU3PluqvnxzRshkX65sTUsPeBwAAnK67/35H/KgRdE9gDlLwEFtgo0gqSAAA2ImA5BCBZf6udiZpsw8SAAD2IiA5RGeTtNkHCQAAexGQHMJv2juslgoSAADhQEByiPY2ioxzs8wfAIBwICA5RBMbRQIA0GcISA7R7mG11io2KkgAANiJgOQQgSJR640i461J2lSQAACwEwHJIVpWsbVcsyZps5M2AAC2IiA5RHur2Kx9kBoZYgMAwE4EJIdoPyCdWsVGBQkAAFsRkBwiMM2ovaNG2CgSAAB7EZAcoqWC1HItlknaAACEBQHJIQKH1brdrVexcVgtAADhQEByiKb2jhpxcxYbAADhQEByiFP5SDHtnMXGTtoAANiLgOQQ7R81EjiLjYAEAICdCEgOYU3Sbn3UiPvUHCQ/Q2wAANiJgOQQ7e2DxCo2AADCg4DkEP5O9kFqooIEAICtCEgOEVjF5mq9DxKr2AAACAsCkkNYh9W2cxYbR40AAGAvApJDBDaKbD3EFmutYqOCBACAnQhIDhGYZuRynb6KjUnaAADYi4DkEE3WEFvLNWsfJCZpAwBgKwKSQ7R3FlusdRYbAQkAADsRkByi3X2Q3OyDBABAOBCQHCKQgdztrmKjggQAgJ0ISA5hAnOQWv3G2EkbAIDwICA5RMtGka0qSG7mIAEAEA4EJIcIjKLFtHMWGxtFAgBgLwKSQ7SsYmu51jLEZqwhOAAA0HsEJIdobxVbXKu0xIG1AADYh4DkEE2dHDUisZINAAA7EZAcIjCC5m7nqBGJgAQAgJ0ISA7R1N5Gka0rSCz1BwDANgQkh2iZg9RyLbbVNw0s9QcAwDYEJIfwtzMHyeVyWSGJpf4AANiHgOQQ7Q2xSa32QqKCBACAbQhIDhEoELndwQEpsNSf40YAALAPAckh2puDJLXeTZsKEgAAdiEgOUQgIMWcNsRGBQkAALsRkBwisFGky9V2iI05SAAA2I2A5BCBjSJj3O1XkFjFBgCAfQhIDhFYxRbT5jfW+sBaAABgDwKSQwTmIJ0+xHaqgkRAAgDANgQkhwiMoJ0+SftUBYkhNgAAbENAcgh/hxtFUkECAMBuBCSHCKxic7f5jbWsYqOCBACAXQhIDtFxBSkwxEYFCQAAuxCQHMLfwTL/uFNDbE3MQQIAwDYEJIewhtjaHjXiZpk/AAB2IyA5REdDbDEs8wcAwHYEJIfw+9sPSHHWYbUMsQEAYJeQAtInn3xidz/QhY7mILUcVksFCQAAu4QUkM477zxdccUV+q//+i+dPHnS7j6hHYGjRtxtJ2mzzB8AANuFFJC2bt2q8ePHq6CgQF6vV3feeac2b94cUgeWL1+u0aNHKyEhQdnZ2V3eZ+3atRozZowSEhI0btw4bdiwIeh1Y4wKCws1fPhwJSYmKjc3V7t27TrtPn/84x+VnZ2txMREDR48WPn5+SH1v68Y08EkbWuIjQoSAAB2CSkgZWZm6sknn9SBAwf0/PPP67PPPtOUKVM0duxYLVmyRIcOHerWfV566SUVFBRo0aJF2rp1qyZMmKC8vDwdPHiw3fZvvfWWZsyYodmzZ2vbtm3Kz89Xfn6+duzYYbV5/PHHtWzZMq1YsUJlZWXq37+/8vLygipd//M//6Nbb71Vs2bN0jvvvKO//OUvuvnmm0N5FH0msIrt9KNGAkNsVJAAALCNscHJkyfNkiVLjMfjMS6Xy3g8HnPrrbeaAwcOdPq+SZMmmbvvvtv6vqmpyaSmppqioqJ22//Lv/yLmTZtWtC17Oxsc+eddxpjjPH7/cbr9ZonnnjCer2qqsp4PB7z4osvGmOMaWhoMCNGjDDPPfdcSJ81oLq62kgy1dXVvbpPd436/noz6vvrzcGak0HXC9e9Z0Z9f715ovhvfdIPAACcrLv/fvdqFduWLVv0b//2bxo+fLiWLFmiBx54QB9//LE2btyoAwcO6LrrruvwvfX19SovL1dubq51ze12Kzc3V6Wlpe2+p7S0NKi9JOXl5Vntd+/eLZ/PF9QmOTlZ2dnZVputW7dq//79crvduvjiizV8+HBdffXVQVWo9tTV1ammpiboq6/4Ww2fdThJm1VsAADYJqSAtGTJEo0bN06TJ0/WgQMHtGrVKu3du1ePPvqo0tPTdfnll2vlypXaunVrh/c4fPiwmpqalJKSEnQ9JSVFPp+v3ff4fL5O2wf+7KxNYAXeD37wAy1cuFDr16/X4MGD9dWvflVHjhzpsL9FRUVKTk62vtLS0jpsa7fAHkhSJ3OQWMUGAIBtQgpIzzzzjG6++Wbt3btX69at07XXXit3m1NUzz77bP3qV7+ypZN28p+qtDz88MO68cYblZWVpRdeeEEul0tr167t8H3z589XdXW19bVv376+6rK1gk1qbxVbYKNIKkgAANglNpQ3bdy4USNHjjwtFBljtG/fPo0cOVLx8fGaOXNmh/cYNmyYYmJiVFlZGXS9srJSXq+33fd4vd5O2wf+rKys1PDhw4PaZGZmSpJ1/cILL7Re93g8Ovfcc1VRUdFhfz0ejzweT4evh1Pr0TMOqwUAIPxCqiB96Utf0uHDh0+7fuTIEaWnp3frHvHx8crKylJJSYl1ze/3q6SkRDk5Oe2+JycnJ6i91BzWAu3T09Pl9XqD2tTU1KisrMxqk5WVJY/Ho507d1ptGhoatGfPHo0aNapbfe9rrYfY2q5iCxxWSwUJAAD7hFRBMqb9asWxY8eUkJDQ7fsUFBRo5syZmjhxoiZNmqSlS5eqtrZWs2bNkiTddtttGjFihIqKiiRJc+fO1dSpU7V48WJNmzZNa9as0ZYtW/Tss89Kklwul+677z49+uijysjIUHp6uh555BGlpqZa+xwlJSXprrvu0qJFi5SWlqZRo0bpiSeekCR9+9vfDuVxhF3rITZXB4fVMgcJAAD79CggFRQUSGoOIoWFherXr5/1WlNTk8rKyqyhrO6YPn26Dh06pMLCQvl8PmVmZqq4uNiaZF1RURE0jDd58mStXr1aCxcu1IIFC5SRkaF169Zp7NixVpuHHnpItbW1uuOOO1RVVaUpU6aouLg4KLg98cQTio2N1a233qoTJ04oOztbmzZt0uDBg3vyOPqMaVUc6ngVGwEJAAC7uExH5aB2XHHFFZKkN954Qzk5OYqPj7dei4+P1+jRo/XAAw8oIyPD/p5GmZqaGiUnJ6u6ulpJSUlh/VlHaut1yY82SpI++X/XBE3UXlW6R4W/e1/XjPPq57dkhbUfAAA4XXf//e5RBem1116TJM2aNUtPPvlk2IMBmvk7HWLjsFoAAOwW0hykF154we5+oBOBjSLdrubhzdZa9kFikjYAAHbpdkC64YYbtHLlSiUlJemGG27otO3LL7/c646hRWB6Udsl/lKrSdrMQQIAwDbdDkjJyclW9SI5OTlsHcLpAqvY2m4SKXFYLQAA4dDtgNR6WI0htr7VeoitrTiW+QMAYLuQNoo8ceKEjh8/bn2/d+9eLV26VK+++qptHUOLwCTttptESizzBwAgHEIKSNddd51WrVolSaqqqtKkSZO0ePFiXXfddXrmmWds7SC6mIPEJG0AAGwXUkDaunWrLr/8cknSb37zG3m9Xu3du1erVq3SsmXLbO0gpCZ/x3OQWg6rpYIEAIBdQgpIx48f18CBAyVJr776qm644Qa53W5ddtll2rt3r60dRMsQW3tzkFoOq6WCBACAXUIKSOedd57WrVunffv26X//93/19a9/XZJ08OBBNo8MA2sOUnsVpBgmaQMAYLeQAlJhYaEeeOABjR49WtnZ2crJyZHUXE26+OKLbe0gWg2xtbsPUmCIjQoSAAB2CWkn7W9961uaMmWKPvvsM02YMMG6fuWVV+r666+3rXNoZroxSZtVbAAA2CekgCRJXq9XXq836NqkSZN63SGcLlBBan+IjQoSAAB2Cykg1dbW6rHHHlNJSYkOHjwof5sJwp988oktnUOzwBykdgpILUeNMAcJAADbhBSQvvOd7+iNN97QrbfequHDh592gCrs1fkk7VMVJIbYAACwTUgB6ZVXXtEf//hHfeUrX7G7P2hldVmFJGnP4VpJ0rGTjda1gCvGnCVJamSZPwAAtglpFdvgwYM1ZMgQu/uCDvjV9Sq2hiYjY6giAQBgh5AC0o9+9CMVFhYGnceG8AnknvZGMgP7IEktk7kBAEDvhDTEtnjxYn388cdKSUnR6NGjFRcXF/T61q1bbekcmnW+zL8l4zb6jWJj+qpXAACcuUIKSPn5+TZ3A53pzio2SWpo8ishjoQEAEBvhRSQFi1aZHc/0InOhthaBySW+gMAYI+Q5iBJUlVVlZ577jnNnz9fR44ckdQ8tLZ//37bOodmxnQ8Sbv10n8OrAUAwB4hVZDeffdd5ebmKjk5WXv27NGcOXM0ZMgQvfzyy6qoqNCqVavs7ucXWqAu1N5uUy6XS3ExLjU0GSpIAADYJKQKUkFBgW6//Xbt2rVLCQkJ1vVrrrlGb775pm2dQ7OWOUjtb8jZcmAtAQkAADuEFJDefvtt3XnnnaddHzFihHw+X687hWCdzUGSWh9YyxAbAAB2CCkgeTwe1dTUnHb973//u84666xedwrB/J3MQZJaH1hLBQkAADuEFJC++c1v6oc//KEaGhokNQ/9VFRU6Pvf/75uvPFGWzuIzucgSS0r2RqaqCABAGCHkALS4sWLdezYMZ111lk6ceKEpk6dqvPOO08DBw7Uj3/8Y7v7+IXX2So2iQNrAQCwW0ir2JKTk7Vx40b95S9/0TvvvKNjx47pkksuUW5urt39g7o/B6mRChIAALbocUDy+/1auXKlXn75Ze3Zs0cul0vp6enyer0yxnS40gqh83cVkKwhNipIAADYoUdDbMYYffOb39R3vvMd7d+/X+PGjdNFF12kvXv36vbbb9f1118frn5+oQWG2FwdzEIKDLFxWC0AAPboUQVp5cqVevPNN1VSUqIrrrgi6LVNmzYpPz9fq1at0m233WZrJ7/oWg6rbf91lvkDAGCvHlWQXnzxRS1YsOC0cCRJX/va1zRv3jz9+te/tq1zaOYXG0UCANCXehSQ3n33XV111VUdvn711VfrnXfe6XWnEKyrSdpxTNIGAMBWPQpIR44cUUpKSoevp6Sk6PPPP+91pxCsq40iAxWkBuYgAQBgix4FpKamJsXGdjxtKSYmRo2Njb3uFIKxzB8AgL7Vo0naxhjdfvvt8ng87b5eV1dnS6cQrNsbRTIHCQAAW/QoIM2cObPLNqxgs1+3jxphFRsAALboUUB64YUXwtUPdKJlo8gO5iBZQ2xUkAAAsENIZ7Ghb1kbRXa4k/apSdrMQQIAwBYEJAfwd3OjSA6rBQDAHgQkBzDq4qgRa6NIKkgAANiBgOQA3V3mz2G1AADYg4DkAF1tFGkt82cVGwAAtiAgOUCXFSQ3q9gAALATAckButooMjYmsIqNgAQAgB0ISA5gVZA6eD1wWG0TQ2wAANiCgOQAfmsfJA6rBQCgLxCQHMA6aoTDagEA6BMEJAfoaqPIOI4aAQDAVgQkBzAMsQEA0KcISA7Q1TL/OIbYAACwFQHJAbo6aoRl/gAA2IuA5ABdHlYb2CiSZf4AANiCgOQAxgpIXRw1QgUJAABbEJAcoGWSdvuvtxxWSwUJAAA7REVAWr58uUaPHq2EhARlZ2dr8+bNnbZfu3atxowZo4SEBI0bN04bNmwIet0Yo8LCQg0fPlyJiYnKzc3Vrl272r1XXV2dMjMz5XK5tH37drs+kq263igyMMRGBQkAADtEPCC99NJLKigo0KJFi7R161ZNmDBBeXl5OnjwYLvt33rrLc2YMUOzZ8/Wtm3blJ+fr/z8fO3YscNq8/jjj2vZsmVasWKFysrK1L9/f+Xl5enkyZOn3e+hhx5Sampq2D6fHayNIjt4PbDMn1VsAADYI+IBacmSJZozZ45mzZqlCy+8UCtWrFC/fv30/PPPt9v+ySef1FVXXaUHH3xQF1xwgX70ox/pkksu0dNPPy2puXq0dOlSLVy4UNddd53Gjx+vVatW6cCBA1q3bl3QvV555RW9+uqr+ulPfxruj9krXU7StobYqCABAGCHiAak+vp6lZeXKzc317rmdruVm5ur0tLSdt9TWloa1F6S8vLyrPa7d++Wz+cLapOcnKzs7Oyge1ZWVmrOnDn6z//8T/Xr16/LvtbV1ammpiboq690tVGkNUmbVWwAANgiogHp8OHDampqUkpKStD1lJQU+Xy+dt/j8/k6bR/4s7M2xhjdfvvtuuuuuzRx4sRu9bWoqEjJycnWV1paWrfeZ4euNoq05iBRQQIAwBYRH2KLhKeeekpHjx7V/Pnzu/2e+fPnq7q62vrat29fGHsYLFBBcnexUSSTtAEAsEdEA9KwYcMUExOjysrKoOuVlZXyer3tvsfr9XbaPvBnZ202bdqk0tJSeTwexcbG6rzzzpMkTZw4UTNnzmz353o8HiUlJQV99RU/R40AANCnIhqQ4uPjlZWVpZKSEuua3+9XSUmJcnJy2n1PTk5OUHtJ2rhxo9U+PT1dXq83qE1NTY3KysqsNsuWLdM777yj7du3a/v27dY2AS+99JJ+/OMf2/oZ7WAdNcJhtQAA9InYSHegoKBAM2fO1MSJEzVp0iQtXbpUtbW1mjVrliTptttu04gRI1RUVCRJmjt3rqZOnarFixdr2rRpWrNmjbZs2aJnn31WUnOIuO+++/Too48qIyND6enpeuSRR5Samqr8/HxJ0siRI4P6MGDAAEnSl770JZ1zzjl99Mm7z3Sxio0KEgAA9op4QJo+fboOHTqkwsJC+Xw+ZWZmqri42JpkXVFRIbe7pdA1efJkrV69WgsXLtSCBQuUkZGhdevWaezYsVabhx56SLW1tbrjjjtUVVWlKVOmqLi4WAkJCX3++ezQMkm7izlITNIGAMAWLhOYAYweqampUXJysqqrq8M2H2l1WYUk6bk/faJPDtdq+qVpmnDOoKA2N2eP1KefH9eUn7wmT6xbOx+9Oix9AQDgTNDdf7+/kKvYnCaQYDs6rDYxLkaSVNfoVxPzkAAA6DUCkgNYZ7F18Hq/+JaR0hMNTX3QIwAAzmwEJAfoapJ2Qpzb2gLgeH1j33QKAIAzGAHJAbo6asTlclnDbCfqqSABANBbBCQH6GqjSEnqF98ckI4TkAAA6DUCkgNYG0V2OAtJSiQgAQBgGwKSA3Q1B0mS+sU1T9RmiA0AgN6L+EaR6FpnG0UG9koKTM4u3uFTxZHj1us3Z4887T0AAKBzVJAcwFrm30kFKT62+VdZz3EjAAD0GgHJAVqG2DpOSPGnjhtpaCQgAQDQWwQkB+hqo0hJijtVQaqjggQAQK8RkByg5aiRjtsEKkj1VJAAAOg1ApIDdLVRpNQyB6mBChIAAL1GQHKA7mwUSQUJAAD7EJAcoCcVJFaxAQDQewQkB+jORpFxVJAAALANAckBApGns6NGmIMEAIB9CEgOYHqyUSQVJAAAeo2A5ACmJ5O0qSABANBrBCQHCGwU2elO2lSQAACwDQHJAVo2iuz6qBEqSAAA9B4ByQFMD44aoYIEAEDvEZAcoCdzkFjFBgBA7xGQHMDfo6NGjNUeAACEhoDkAN3ZKDJQQZKoIgEA0FsEpChnjLEmaXdWQYqNaXmNeUgAAPQOASnKtR4s6+yX5Xa5FHcqJDU0McQGAEBvEJCiXOvpRJ1VkKRWS/2pIAEA0CsEpCjXesJ1F/moZbNI5iABANArBKQoF1xB6rwtu2kDAGAPAlKUM60SUmc7aUsMsQEAYBcCUpRrPd26qwpSHENsAADYgoAU5YLmIHV62Eir3bSpIAEA0CsEpCjXeg5SZxtFSkzSBgDALgSkKBe8iq2bc5AISAAA9AoBKcoF4lFX1SOp1RwkhtgAAOgVAlKUCxSQupp/JFFBAgDALgSkKBcYYutqBZvUMgeJSdoAAPQOASnKWRWk7gSkUxWkOgISAAC9QkCKcoGNIrvaJFJqVUFiiA0AgF4hIEW5UCpITNIGAKB3CEhRzpqD1J1J2uyDBACALQhIUa5Hy/ypIAEAYAsCUpRrWcXGHCQAAPoKASnKBeYgdaeCxBwkAADsQUCKco2nqkGxMV3/qpiDBACAPQhIUa6+qbmEFBfTdQkp0KahyQSd4QYAAHqGgBTlAhWkuB5UkCTmIQEA0BsEpChX34OA1LoN85AAAAgdASnKNZwaYovvRkByu1xBw2wAACA0BKQo12BVkLqxjE2sZAMAwA4EpCjX0IMhNomVbAAA2IGAFOWsOUix3ftVeWJjJEl1DU1h6xMAAGc6AlKUa2js/hwkSUqIa253goAEAEDICEhRrqdzkBLiAhUkhtgAAAgVASnK9XQOUiAgnWykggQAQKgISFGuJ/sgSS1DbCcZYgMAIGRREZCWL1+u0aNHKyEhQdnZ2dq8eXOn7deuXasxY8YoISFB48aN04YNG4JeN8aosLBQw4cPV2JionJzc7Vr1y7r9T179mj27NlKT09XYmKivvSlL2nRokWqr68Py+frjQbrqJFuBqRTk7RPMsQGAEDIIh6QXnrpJRUUFGjRokXaunWrJkyYoLy8PB08eLDd9m+99ZZmzJih2bNna9u2bcrPz1d+fr527NhhtXn88ce1bNkyrVixQmVlZerfv7/y8vJ08uRJSdLf/vY3+f1+/eIXv9D777+vn/3sZ1qxYoUWLFjQJ5+5J0Kdg0QFCQCA0LmMieypptnZ2br00kv19NNPS5L8fr/S0tJ07733at68eae1nz59umpra7V+/Xrr2mWXXabMzEytWLFCxhilpqbq/vvv1wMPPCBJqq6uVkpKilauXKmbbrqp3X488cQTeuaZZ/TJJ5+0+3pdXZ3q6uqs72tqapSWlqbq6molJSWF/Pk7s7qsQr9442PtPXJcN08aqbEjkrt8T9nuf+h32w/oAu9A3ZozWjdnjwxL3wAAcKKamholJyd3+e93RCtI9fX1Ki8vV25urnXN7XYrNzdXpaWl7b6ntLQ0qL0k5eXlWe13794tn88X1CY5OVnZ2dkd3lNqDlFDhgzp8PWioiIlJydbX2lpad36jL0VqCDFd3MfpJZJ2gyxAQAQqogGpMOHD6upqUkpKSlB11NSUuTz+dp9j8/n67R94M+e3POjjz7SU089pTvvvLPDvs6fP1/V1dXW1759+zr/cDap7+EcpESG2AAA6LXYSHcg0vbv36+rrrpK3/72tzVnzpwO23k8Hnk8nj7sWbMez0GKZRUbAAC9FdEK0rBhwxQTE6PKysqg65WVlfJ6ve2+x+v1dto+8Gd37nngwAFdccUVmjx5sp599tlefZZw6ek+SJ44VrEBANBbEQ1I8fHxysrKUklJiXXN7/erpKREOTk57b4nJycnqL0kbdy40Wqfnp4ur9cb1KampkZlZWVB99y/f7+++tWvKisrSy+88ILc7ogv6GuXNQephxtF1jU2KcLz7wEAcKyID7EVFBRo5syZmjhxoiZNmqSlS5eqtrZWs2bNkiTddtttGjFihIqKiiRJc+fO1dSpU7V48WJNmzZNa9as0ZYtW6wKkMvl0n333adHH31UGRkZSk9P1yOPPKLU1FTl5+dLaglHo0aN0k9/+lMdOnTI6k9HlatIMMa07IPU7Unaze38pmWTSQAA0DMRD0jTp0/XoUOHVFhYKJ/Pp8zMTBUXF1uTrCsqKoKqO5MnT9bq1au1cOFCLViwQBkZGVq3bp3Gjh1rtXnooYdUW1urO+64Q1VVVZoyZYqKi4uVkJAgqbni9NFHH+mjjz7SOeecE9SfaKq6NPpb+tLdOUjxMW65Xc0BiWE2AABCE/F9kJyqu/so9MZzb36iRzd8KEl6NH+s3K7uhaQfrf9AJxqaNPfKDH3vn78clr4BAOBEjtgHCZ0LDJHFul3dDkdSyzBbHSvZAAAICQEpivX0HLYANosEAKB3CEhRrKd7IAUEAtIJKkgAAISEgBTFeroHUgAH1gIA0DsEpChW38Nz2AICu2nXsYoNAICQEJCiWENjL+cgUUECACAkBKQoFhhii+3xHKRT57E1EpAAAAgFASmK9fSYkYAEzmMDAKBXCEhRrJ5J2gAARAQBKYr1eh8kAhIAACEhIEWxkPdBOrWKjSE2AABCQ0CKYg2NvZyDxCRtAABCQkCKYtYcpB7ug+QJrGJjiA0AgJAQkKJYqHOQEk9VkOoa/PL7je39AgDgTEdAimK9PYvNSKqtb7S7WwAAnPEISFEs1H2QYt0uxbiaQ9XRkwQkAAB6ioAUxUI9rNblclnzkAhIAAD0HAEpioU6B0lqGWY7erLB1j4BAPBFQECKYlYFKbZnc5CklonaNQQkAAB6jIAUxepD3AdJEkNsAAD0AgEpioU6B0mSEmIDFSQCEgAAPUVAimJ2zEGqOcEQGwAAPUVAimKh7oMkSUkJsZKkz6pP2NonAAC+CAhIUarJb9R4ahfsUOYgDR0QL0nac/i4rf0CAOCLgIAUpVqfoxYbSkDq75Ek7flHrW19AgDgi4KAFKVOBAWkng+xBSpIB6pOqK6RQ2sBAOgJAlKUOlHfHGriYlxyu3oekAZ4YhUf65bfSPuOMMwGAEBPEJCiVGCILZQVbFLzcSPD+jMPCQCAUBCQotSJXgYkSRo6gHlIAACEgoAUpVqG2HoRkE5VkHYfJiABANATBKQoFaggxYcwQTsgUEHa+w+G2AAA6AkCUpTq7RwkSRo2gAoSAAChICBFqZMNp3bRjg39VzTk1BDbgeoTQfsqAQCAzhGQopQdk7QHeGI1wBMrY6RPP2eYDQCA7iIgRanW+yCFyuVyafSwfpKk3Sz1BwCg2whIUarqRIMkKSEuplf3GTW0vyRpD/OQAADoNgJSlDp0tE6SNNAT26v7pAcCEnshAQDQbQSkKHX4WHNAGpDQu4A0amjzENsnhwhIAAB0FwEpStlVQRrjTZIkfeirkTGm1/0CAOCLgIAUpawKUi8D0pe9AxQX41LV8QbtrzphR9cAADjjEZCikDHGqiANSIjr1b08sTH6cspASdKO/dW97hsAAF8EBKQodKyuUXWNzRtF9raCJEljU5MlSTv21/T6XgAAfBEQkKJQoHrkiXUrvhc7aQeMPedUQDpABQkAgO4gIEWhw8fqJdlTPZKksanNE7V37K9mojYAAN1AQIpCLfOP7AlIFwxPUozbpcPH6lVZU2fLPQEAOJMRkKKQXSvYAhLiYpRx9gBJTNQGAKA7CEhRyNoDyaYKkiRddGqi9nsEJAAAukRAikJ2V5AkaeyI5nlI7zNRGwCALhGQolDLLtq92wOptXEjqCABANBdBKQoZNc5bK1dmJqkuBiXKmvqtNN31Lb7AgBwJrLvX2DYxlrFZsMQ2+qyCuu/zzt7oD78rEb/b8OHyrvIa12/OXtkr38OAABnEipIUcYY07IPko0VJEnKTBskSXrn0yr52Q8JAIAOEZCiTM2JRtU32XfMSGtjvAPliXWr6niDKv5x3NZ7AwBwJiEgRZlDx1qW+MfF2PvriYtx66JTu2pv/7TK1nsDAHAmISBFmcD8o7MGeMJy/wmnhtne+7RajX5/WH4GAABOR0CKMoEVbMMGhicgfemsARroidWJhiatLqvQifqmsPwcAACcLCoC0vLlyzV69GglJCQoOztbmzdv7rT92rVrNWbMGCUkJGjcuHHasGFD0OvGGBUWFmr48OFKTExUbm6udu3aFdTmyJEjuuWWW5SUlKRBgwZp9uzZOnbsmO2frafCXUFyu1y6dkKqYt0u/c13VMtf/0jlez8Py88CAMCpIh6QXnrpJRUUFGjRokXaunWrJkyYoLy8PB08eLDd9m+99ZZmzJih2bNna9u2bcrPz1d+fr527NhhtXn88ce1bNkyrVixQmVlZerfv7/y8vJ08uRJq80tt9yi999/Xxs3btT69ev15ptv6o477gj75+1KoIJ0VpgqSFLzppF3/tOXNLhfnI7U1uvGZ97S/Jff08Gak12/GQCALwCXMZFd752dna1LL71UTz/9tCTJ7/crLS1N9957r+bNm3da++nTp6u2tlbr16+3rl122WXKzMzUihUrZIxRamqq7r//fj3wwAOSpOrqaqWkpGjlypW66aab9OGHH+rCCy/U22+/rYkTJ0qSiouLdc011+jTTz9Vampql/2uqalRcnKyqqurlZSUZMejkCQ9uPYdrS3/VA98/csa0j98IUmSTtQ3acN7n6m8oqWCNHZEksaNGKR+8TFKjItRYnyMEuIC/+1WjLs5U7tOtXed+g/XqSst3we/DgBAT2WNGmJ7waC7/35HdKPI+vp6lZeXa/78+dY1t9ut3NxclZaWtvue0tJSFRQUBF3Ly8vTunXrJEm7d++Wz+dTbm6u9XpycrKys7NVWlqqm266SaWlpRo0aJAVjiQpNzdXbrdbZWVluv7660/7uXV1daqrq7O+r65uPrKjpqam5x+8EwcO/UP+uuPqp3odr6239d7tuXpMsi48K07/94FP+6tO6t1PjuvdT3xh/7kAAHTlF7dm6SvnDbP1noF/t7uqD0U0IB0+fFhNTU1KSUkJup6SkqK//e1v7b7H5/O1297n81mvB6511ubss88Oej02NlZDhgyx2rRVVFSkf//3fz/telpaWkcfr1dmLw3LbQEAcIxrlobv3kePHlVycnKHr3PUSDfNnz8/qHLl9/t15MgRDR06VK4wjCPV1NQoLS1N+/bts3UI74uIZ2kfnqV9eJb24Vna54vwLI0xOnr0aJfTaSIakIYNG6aYmBhVVlYGXa+srJTX6233PV6vt9P2gT8rKys1fPjwoDaZmZlWm7aTwBsbG3XkyJEOf67H45HHEzwOOmjQoM4/oA2SkpLO2L+kfY1naR+epX14lvbhWdrnTH+WnVWOAiK6ii0+Pl5ZWVkqKSmxrvn9fpWUlCgnJ6fd9+Tk5AS1l6SNGzda7dPT0+X1eoPa1NTUqKyszGqTk5OjqqoqlZeXW202bdokv9+v7Oxs2z4fAABwpogPsRUUFGjmzJmaOHGiJk2apKVLl6q2tlazZs2SJN12220aMWKEioqKJElz587V1KlTtXjxYk2bNk1r1qzRli1b9Oyzz0qSXC6X7rvvPj366KPKyMhQenq6HnnkEaWmpio/P1+SdMEFF+iqq67SnDlztGLFCjU0NOiee+7RTTfd1K0VbAAA4MwW8YA0ffp0HTp0SIWFhfL5fMrMzFRxcbE1ybqiokJud0uha/LkyVq9erUWLlyoBQsWKCMjQ+vWrdPYsWOtNg899JBqa2t1xx13qKqqSlOmTFFxcbESEhKsNr/+9a91zz336Morr5Tb7daNN96oZcuW9d0H74LH49GiRYtOG9ZDz/Es7cOztA/P0j48S/vwLFtEfB8kAACAaBPxnbQBAACiDQEJAACgDQISAABAGwQkAACANghIUWr58uUaPXq0EhISlJ2drc2bN0e6SxH15ptv6hvf+IZSU1Plcrmss/cCjDEqLCzU8OHDlZiYqNzcXO3atSuozZEjR3TLLbcoKSlJgwYN0uzZs3Xs2LGgNu+++64uv/xyJSQkKC0tTY8//ni4P1qfKioq0qWXXqqBAwfq7LPPVn5+vnbu3BnU5uTJk7r77rs1dOhQDRgwQDfeeONpm7NWVFRo2rRp6tevn84++2w9+OCDamxsDGrz+uuv65JLLpHH49F5552nlStXhvvj9alnnnlG48ePtzbUy8nJ0SuvvGK9znMM3WOPPWZt2RLA8+yeH/zgB3K5XEFfY8aMsV7nOfaAQdRZs2aNiY+PN88//7x5//33zZw5c8ygQYNMZWVlpLsWMRs2bDAPP/ywefnll40k89vf/jbo9ccee8wkJyebdevWmXfeecd885vfNOnp6ebEiRNWm6uuuspMmDDB/PWvfzV/+tOfzHnnnWdmzJhhvV5dXW1SUlLMLbfcYnbs2GFefPFFk5iYaH7xi1/01ccMu7y8PPPCCy+YHTt2mO3bt5trrrnGjBw50hw7dsxqc9ddd5m0tDRTUlJitmzZYi677DIzefJk6/XGxkYzduxYk5uba7Zt22Y2bNhghg0bZubPn2+1+eSTT0y/fv1MQUGB+eCDD8xTTz1lYmJiTHFxcZ9+3nD6/e9/b/74xz+av//972bnzp1mwYIFJi4uzuzYscMYw3MM1ebNm83o0aPN+PHjzdy5c63rPM/uWbRokbnooovMZ599Zn0dOnTIep3n2H0EpCg0adIkc/fdd1vfNzU1mdTUVFNUVBTBXkWPtgHJ7/cbr9drnnjiCetaVVWV8Xg85sUXXzTGGPPBBx8YSebtt9+22rzyyivG5XKZ/fv3G2OM+fnPf24GDx5s6urqrDbf//73zfnnnx/mTxQ5Bw8eNJLMG2+8YYxpfm5xcXFm7dq1VpsPP/zQSDKlpaXGmOaw6na7jc/ns9o888wzJikpyXp2Dz30kLnooouCftb06dNNXl5euD9SRA0ePNg899xzPMcQHT161GRkZJiNGzeaqVOnWgGJ59l9ixYtMhMmTGj3NZ5jzzDEFmXq6+tVXl6u3Nxc65rb7VZubq5KS0sj2LPotXv3bvl8vqBnlpycrOzsbOuZlZaWatCgQZo4caLVJjc3V263W2VlZVabf/qnf1J8fLzVJi8vTzt37tTnn3/eR5+mb1VXV0uShgwZIkkqLy9XQ0ND0LMcM2aMRo4cGfQsx40bZ23mKjU/p5qaGr3//vtWm9b3CLQ5U/8ONzU1ac2aNaqtrVVOTg7PMUR33323pk2bdtpn5nn2zK5du5Samqpzzz1Xt9xyiyoqKiTxHHuKgBRlDh8+rKampqC/nJKUkpIin88XoV5Ft8Bz6eyZ+Xw+nX322UGvx8bGasiQIUFt2rtH659xJvH7/brvvvv0la98xdqJ3ufzKT4+/rSDmNs+y66eU0dtampqdOLEiXB8nIh47733NGDAAHk8Ht1111367W9/qwsvvJDnGII1a9Zo69at1rFSrfE8uy87O1srV65UcXGxnnnmGe3evVuXX365jh49ynPsoYgfNQIgMu6++27t2LFDf/7znyPdFcc6//zztX37dlVXV+s3v/mNZs6cqTfeeCPS3XKcffv2ae7cudq4cWPQkVDouauvvtr67/Hjxys7O1ujRo3Sf//3fysxMTGCPXMeKkhRZtiwYYqJiTltVUFlZaW8Xm+EehXdAs+ls2fm9Xp18ODBoNcbGxt15MiRoDbt3aP1zzhT3HPPPVq/fr1ee+01nXPOOdZ1r9er+vp6VVVVBbVv+yy7ek4dtUlKSjqj/icdHx+v8847T1lZWSoqKtKECRP05JNP8hx7qLy8XAcPHtQll1yi2NhYxcbG6o033tCyZcsUGxurlJQUnmeIBg0apC9/+cv66KOP+HvZQwSkKBMfH6+srCyVlJRY1/x+v0pKSpSTkxPBnkWv9PR0eb3eoGdWU1OjsrIy65nl5OSoqqpK5eXlVptNmzbJ7/crOzvbavPmm2+qoaHBarNx40adf/75Gjx4cB99mvAyxuiee+7Rb3/7W23atEnp6elBr2dlZSkuLi7oWe7cuVMVFRVBz/K9994LCpwbN25UUlKSLrzwQqtN63sE2pzpf4f9fr/q6up4jj105ZVX6r333tP27dutr4kTJ+qWW26x/pvnGZpjx47p448/1vDhw/l72VORniWO061Zs8Z4PB6zcuVK88EHH5g77rjDDBo0KGhVwRfN0aNHzbZt28y2bduMJLNkyRKzbds2s3fvXmNM8zL/QYMGmd/97nfm3XffNdddd127y/wvvvhiU1ZWZv785z+bjIyMoGX+VVVVJiUlxdx6661mx44dZs2aNaZfv35n1DL/7373uyY5Odm8/vrrQcuAjx8/brW56667zMiRI82mTZvMli1bTE5OjsnJybFeDywD/vrXv262b99uiouLzVlnndXuMuAHH3zQfPjhh2b58uVn3DLgefPmmTfeeMPs3r3bvPvuu2bevHnG5XKZV1991RjDc+yt1qvYjOF5dtf9999vXn/9dbN7927zl7/8xeTm5pphw4aZgwcPGmN4jj1BQIpSTz31lBk5cqSJj483kyZNMn/9618j3aWIeu2114yk075mzpxpjGle6v/II4+YlJQU4/F4zJVXXml27twZdI9//OMfZsaMGWbAgAEmKSnJzJo1yxw9ejSozTvvvGOmTJliPB6PGTFihHnsscf66iP2ifaeoSTzwgsvWG1OnDhh/u3f/s0MHjzY9OvXz1x//fXms88+C7rPnj17zNVXX20SExPNsGHDzP33328aGhqC2rz22msmMzPTxMfHm3PPPTfoZ5wJ/vVf/9WMGjXKxMfHm7POOstceeWVVjgyhufYW20DEs+ze6ZPn26GDx9u4uPjzYgRI8z06dPNRx99ZL3Oc+w+lzHGRKZ2BQAAEJ2YgwQAANAGAQkAAKANAhIAAEAbBCQAAIA2CEgAAABtEJAAAADaICABAAC0QUACAABog4AEAJ3Ys2ePXC6Xtm/fHumuAOhDBCQAAIA2CEgA0AvGGDU2Nka6GwBsRkAC8IVSXFysKVOmaNCgQRo6dKiuvfZaffzxx9brmzdv1sUXX6yEhARNnDhR27ZtC3r/66+/LpfLpVdeeUVZWVnyeDz685//3NcfA0CYEZAAfKHU1taqoKBAW7ZsUUlJidxut66//nr5/X4dO3ZM1157rS688EKVl5frBz/4gR544IF27zNv3jw99thj+vDDDzV+/Pg+/hQAwi020h0AgL504403Bn3//PPP66yzztIHH3ygt956S36/X7/61a+UkJCgiy66SJ9++qm++93vnnafH/7wh/rnf/7nvuo2gD5GBQnAF8quXbs0Y8YMnXvuuUpKStLo0aMlSRUVFVY1KCEhwWqfk5PT7n0mTpzYF90FECFUkAB8oXzjG9/QqFGj9Mtf/lKpqany+/0aO3as6uvre3Sf/v37h6mHAKIBFSQAXxj/+Mc/tHPnTi1cuFBXXnmlLrjgAn3++efW6xdccIHeffddnTx50rr217/+NRJdBRBhBCQAXxiDBw/W0KFD9eyzz+qjjz7Spk2bVFBQYL1+8803y+Vyac6cOfrggw+0YcMG/fSnP41gjwFECgEJwBeG2+3WmjVrVF5errFjx+p73/uennjiCev1AQMG6A9/+IPee+89XXzxxXr44Yf1k5/8JII9BhApLmOMiXQnAAAAogkVJAAAgDYISAAAAG0QkAAAANogIAEAALRBQAIAAGiDgAQAANAGAQkAAKANAhIAAEAbBCQAAIA2CEgAAABtEJAAAADa+P/A7H88YbeXDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dataframe['adr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "bc2b7ca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>customer_type</th>\n",
       "      <th>reservation_status</th>\n",
       "      <th>...</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>is_family</th>\n",
       "      <th>total_customer</th>\n",
       "      <th>total_nights</th>\n",
       "      <th>deposit_given</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14989</th>\n",
       "      <td>0.277674</td>\n",
       "      <td>0.322277</td>\n",
       "      <td>0.374106</td>\n",
       "      <td>0.20231</td>\n",
       "      <td>0.611086</td>\n",
       "      <td>0.174868</td>\n",
       "      <td>0.391567</td>\n",
       "      <td>0.352528</td>\n",
       "      <td>0.254504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-6.38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          hotel  arrival_date_month      meal  country  market_segment  \\\n",
       "14989  0.277674            0.322277  0.374106  0.20231        0.611086   \n",
       "\n",
       "       distribution_channel  reserved_room_type  assigned_room_type  \\\n",
       "14989              0.174868            0.391567            0.352528   \n",
       "\n",
       "       customer_type  reservation_status  ...  previous_bookings_not_canceled  \\\n",
       "14989       0.254504                 0.0  ...                               2   \n",
       "\n",
       "       booking_changes  days_in_waiting_list   adr  \\\n",
       "14989                2                     0 -6.38   \n",
       "\n",
       "       required_car_parking_spaces  total_of_special_requests  is_family  \\\n",
       "14989                            0                          0          0   \n",
       "\n",
       "       total_customer  total_nights  deposit_given  \n",
       "14989             2.0            10              0  \n",
       "\n",
       "[1 rows x 30 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe[dataframe['adr']<0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ad29d4cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "handle_outlier('adr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "36b8d100",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe['adr'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "3119dd20",
   "metadata": {},
   "outputs": [],
   "source": [
    "### now this missing value , as we have already deal with the missing values..'\n",
    "### bcz we have negative value in 'adr' feature as '-6.38'  ,& if we apply ln(1+x) , we will get 'nan'\n",
    "## bcz log wont take negative values.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "d0307df3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='adr', ylabel='Density'>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dataframe['adr'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46448de5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4e82779",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d53588c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "806e58e4",
   "metadata": {},
   "source": [
    "# 11.. Select important Features using Co-relation & univariate analysis.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "01f5efa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x15290c22f50>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 391.486x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(data,hue='is_canceled',xlim=(0,500)).map(sns.kdeplot,'lead_time',shade=True).add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "42a5d4d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr=dataframe.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "b581a0b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>customer_type</th>\n",
       "      <th>reservation_status</th>\n",
       "      <th>...</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>is_family</th>\n",
       "      <th>total_customer</th>\n",
       "      <th>total_nights</th>\n",
       "      <th>deposit_given</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hotel</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.051197</td>\n",
       "      <td>0.061782</td>\n",
       "      <td>-0.040609</td>\n",
       "      <td>0.102592</td>\n",
       "      <td>0.182266</td>\n",
       "      <td>0.198171</td>\n",
       "      <td>0.280884</td>\n",
       "      <td>-0.008378</td>\n",
       "      <td>0.137082</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004467</td>\n",
       "      <td>-0.076598</td>\n",
       "      <td>0.072725</td>\n",
       "      <td>0.154041</td>\n",
       "      <td>-0.218961</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.058094</td>\n",
       "      <td>-0.038762</td>\n",
       "      <td>-0.249747</td>\n",
       "      <td>0.172415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_month</th>\n",
       "      <td>0.051197</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010208</td>\n",
       "      <td>-0.052405</td>\n",
       "      <td>0.047902</td>\n",
       "      <td>0.077075</td>\n",
       "      <td>-0.038801</td>\n",
       "      <td>0.015456</td>\n",
       "      <td>-0.029636</td>\n",
       "      <td>0.069886</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021971</td>\n",
       "      <td>-0.011049</td>\n",
       "      <td>0.029164</td>\n",
       "      <td>0.228179</td>\n",
       "      <td>-0.021910</td>\n",
       "      <td>0.002763</td>\n",
       "      <td>0.020491</td>\n",
       "      <td>0.079090</td>\n",
       "      <td>0.045648</td>\n",
       "      <td>0.057361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meal</th>\n",
       "      <td>0.061782</td>\n",
       "      <td>-0.010208</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.022476</td>\n",
       "      <td>-0.026007</td>\n",
       "      <td>-0.025486</td>\n",
       "      <td>0.010035</td>\n",
       "      <td>0.013913</td>\n",
       "      <td>0.105767</td>\n",
       "      <td>0.050584</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011109</td>\n",
       "      <td>-0.027189</td>\n",
       "      <td>-0.031562</td>\n",
       "      <td>-0.019908</td>\n",
       "      <td>0.003455</td>\n",
       "      <td>0.006875</td>\n",
       "      <td>0.001466</td>\n",
       "      <td>-0.003447</td>\n",
       "      <td>-0.055689</td>\n",
       "      <td>0.047597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>-0.040609</td>\n",
       "      <td>-0.052405</td>\n",
       "      <td>0.022476</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.078982</td>\n",
       "      <td>-0.129774</td>\n",
       "      <td>0.153177</td>\n",
       "      <td>0.125415</td>\n",
       "      <td>-0.029320</td>\n",
       "      <td>0.357232</td>\n",
       "      <td>...</td>\n",
       "      <td>0.091795</td>\n",
       "      <td>-0.061513</td>\n",
       "      <td>0.067054</td>\n",
       "      <td>-0.160520</td>\n",
       "      <td>0.007613</td>\n",
       "      <td>-0.200822</td>\n",
       "      <td>-0.044139</td>\n",
       "      <td>-0.117351</td>\n",
       "      <td>-0.160759</td>\n",
       "      <td>0.395685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>market_segment</th>\n",
       "      <td>0.102592</td>\n",
       "      <td>0.047902</td>\n",
       "      <td>-0.026007</td>\n",
       "      <td>0.078982</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.540116</td>\n",
       "      <td>0.182813</td>\n",
       "      <td>0.209272</td>\n",
       "      <td>-0.265606</td>\n",
       "      <td>0.267006</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.117519</td>\n",
       "      <td>-0.057578</td>\n",
       "      <td>0.096385</td>\n",
       "      <td>-0.004009</td>\n",
       "      <td>-0.145726</td>\n",
       "      <td>-0.155463</td>\n",
       "      <td>-0.104584</td>\n",
       "      <td>-0.023874</td>\n",
       "      <td>-0.004546</td>\n",
       "      <td>0.397229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distribution_channel</th>\n",
       "      <td>0.182266</td>\n",
       "      <td>0.077075</td>\n",
       "      <td>-0.025486</td>\n",
       "      <td>-0.129774</td>\n",
       "      <td>0.540116</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.070381</td>\n",
       "      <td>0.118970</td>\n",
       "      <td>-0.023234</td>\n",
       "      <td>0.177167</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.142650</td>\n",
       "      <td>-0.118461</td>\n",
       "      <td>0.048001</td>\n",
       "      <td>0.126516</td>\n",
       "      <td>-0.146562</td>\n",
       "      <td>0.075047</td>\n",
       "      <td>-0.026278</td>\n",
       "      <td>0.087776</td>\n",
       "      <td>0.084999</td>\n",
       "      <td>0.115160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reserved_room_type</th>\n",
       "      <td>0.198171</td>\n",
       "      <td>-0.038801</td>\n",
       "      <td>0.010035</td>\n",
       "      <td>0.153177</td>\n",
       "      <td>0.182813</td>\n",
       "      <td>0.070381</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.722724</td>\n",
       "      <td>-0.143390</td>\n",
       "      <td>0.072769</td>\n",
       "      <td>...</td>\n",
       "      <td>0.022533</td>\n",
       "      <td>-0.040134</td>\n",
       "      <td>0.068336</td>\n",
       "      <td>-0.185932</td>\n",
       "      <td>-0.093401</td>\n",
       "      <td>-0.161406</td>\n",
       "      <td>-0.133748</td>\n",
       "      <td>-0.246347</td>\n",
       "      <td>-0.188866</td>\n",
       "      <td>0.201923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>assigned_room_type</th>\n",
       "      <td>0.280884</td>\n",
       "      <td>0.015456</td>\n",
       "      <td>0.013913</td>\n",
       "      <td>0.125415</td>\n",
       "      <td>0.209272</td>\n",
       "      <td>0.118970</td>\n",
       "      <td>0.722724</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084025</td>\n",
       "      <td>0.201570</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003205</td>\n",
       "      <td>-0.096277</td>\n",
       "      <td>0.067129</td>\n",
       "      <td>-0.038347</td>\n",
       "      <td>-0.131035</td>\n",
       "      <td>-0.135337</td>\n",
       "      <td>-0.179356</td>\n",
       "      <td>-0.229081</td>\n",
       "      <td>-0.103571</td>\n",
       "      <td>0.263383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_type</th>\n",
       "      <td>-0.008378</td>\n",
       "      <td>-0.029636</td>\n",
       "      <td>0.105767</td>\n",
       "      <td>-0.029320</td>\n",
       "      <td>-0.265606</td>\n",
       "      <td>-0.023234</td>\n",
       "      <td>-0.143390</td>\n",
       "      <td>-0.084025</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.136617</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024716</td>\n",
       "      <td>-0.095486</td>\n",
       "      <td>-0.101833</td>\n",
       "      <td>0.117592</td>\n",
       "      <td>0.061786</td>\n",
       "      <td>0.137114</td>\n",
       "      <td>0.106618</td>\n",
       "      <td>0.124318</td>\n",
       "      <td>0.037720</td>\n",
       "      <td>0.121789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reservation_status</th>\n",
       "      <td>0.137082</td>\n",
       "      <td>0.069886</td>\n",
       "      <td>0.050584</td>\n",
       "      <td>0.357232</td>\n",
       "      <td>0.267006</td>\n",
       "      <td>0.177167</td>\n",
       "      <td>0.072769</td>\n",
       "      <td>0.201570</td>\n",
       "      <td>0.136617</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.057365</td>\n",
       "      <td>-0.144832</td>\n",
       "      <td>0.054301</td>\n",
       "      <td>0.081660</td>\n",
       "      <td>-0.195701</td>\n",
       "      <td>-0.234877</td>\n",
       "      <td>-0.013226</td>\n",
       "      <td>0.044826</td>\n",
       "      <td>0.018554</td>\n",
       "      <td>0.481507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reservation_status_date</th>\n",
       "      <td>0.107481</td>\n",
       "      <td>-0.089512</td>\n",
       "      <td>0.038298</td>\n",
       "      <td>0.340400</td>\n",
       "      <td>0.276512</td>\n",
       "      <td>0.081435</td>\n",
       "      <td>0.148830</td>\n",
       "      <td>0.191989</td>\n",
       "      <td>0.055937</td>\n",
       "      <td>0.488307</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007066</td>\n",
       "      <td>-0.111565</td>\n",
       "      <td>0.042947</td>\n",
       "      <td>-0.106156</td>\n",
       "      <td>-0.087884</td>\n",
       "      <td>-0.218839</td>\n",
       "      <td>-0.084130</td>\n",
       "      <td>-0.057482</td>\n",
       "      <td>-0.063224</td>\n",
       "      <td>0.555202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_canceled</th>\n",
       "      <td>0.137082</td>\n",
       "      <td>0.069886</td>\n",
       "      <td>0.050584</td>\n",
       "      <td>0.357232</td>\n",
       "      <td>0.267006</td>\n",
       "      <td>0.177167</td>\n",
       "      <td>0.072769</td>\n",
       "      <td>0.201570</td>\n",
       "      <td>0.136617</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.057365</td>\n",
       "      <td>-0.144832</td>\n",
       "      <td>0.054301</td>\n",
       "      <td>0.081660</td>\n",
       "      <td>-0.195701</td>\n",
       "      <td>-0.234877</td>\n",
       "      <td>-0.013226</td>\n",
       "      <td>0.044826</td>\n",
       "      <td>0.018554</td>\n",
       "      <td>0.481507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lead_time</th>\n",
       "      <td>0.109869</td>\n",
       "      <td>0.241413</td>\n",
       "      <td>-0.040315</td>\n",
       "      <td>-0.071919</td>\n",
       "      <td>0.397346</td>\n",
       "      <td>0.332215</td>\n",
       "      <td>0.026146</td>\n",
       "      <td>0.142173</td>\n",
       "      <td>-0.135881</td>\n",
       "      <td>0.320075</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.121761</td>\n",
       "      <td>0.006506</td>\n",
       "      <td>0.124403</td>\n",
       "      <td>0.139263</td>\n",
       "      <td>-0.148886</td>\n",
       "      <td>-0.029317</td>\n",
       "      <td>-0.009693</td>\n",
       "      <td>0.136837</td>\n",
       "      <td>0.277977</td>\n",
       "      <td>0.292830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_year</th>\n",
       "      <td>0.035176</td>\n",
       "      <td>0.015509</td>\n",
       "      <td>-0.024568</td>\n",
       "      <td>-0.180684</td>\n",
       "      <td>-0.064498</td>\n",
       "      <td>0.013924</td>\n",
       "      <td>-0.092503</td>\n",
       "      <td>-0.025296</td>\n",
       "      <td>0.204725</td>\n",
       "      <td>0.016622</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029234</td>\n",
       "      <td>0.031416</td>\n",
       "      <td>-0.056348</td>\n",
       "      <td>0.160043</td>\n",
       "      <td>-0.013812</td>\n",
       "      <td>0.108610</td>\n",
       "      <td>0.052787</td>\n",
       "      <td>0.052780</td>\n",
       "      <td>0.031796</td>\n",
       "      <td>-0.065975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_week_number</th>\n",
       "      <td>0.001241</td>\n",
       "      <td>0.155653</td>\n",
       "      <td>0.015308</td>\n",
       "      <td>0.017322</td>\n",
       "      <td>0.009895</td>\n",
       "      <td>0.010940</td>\n",
       "      <td>0.010119</td>\n",
       "      <td>0.005852</td>\n",
       "      <td>-0.067980</td>\n",
       "      <td>0.008315</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021009</td>\n",
       "      <td>0.006311</td>\n",
       "      <td>0.022677</td>\n",
       "      <td>0.036842</td>\n",
       "      <td>0.001980</td>\n",
       "      <td>0.026202</td>\n",
       "      <td>0.010661</td>\n",
       "      <td>0.025752</td>\n",
       "      <td>0.019279</td>\n",
       "      <td>0.007831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <td>-0.001678</td>\n",
       "      <td>0.002248</td>\n",
       "      <td>-0.005737</td>\n",
       "      <td>0.001898</td>\n",
       "      <td>0.008062</td>\n",
       "      <td>-0.003948</td>\n",
       "      <td>-0.016947</td>\n",
       "      <td>-0.011957</td>\n",
       "      <td>-0.002012</td>\n",
       "      <td>-0.005948</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000306</td>\n",
       "      <td>0.011266</td>\n",
       "      <td>0.022532</td>\n",
       "      <td>0.021400</td>\n",
       "      <td>0.008569</td>\n",
       "      <td>0.003026</td>\n",
       "      <td>0.014706</td>\n",
       "      <td>0.006637</td>\n",
       "      <td>-0.027533</td>\n",
       "      <td>-0.008643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <td>-0.187816</td>\n",
       "      <td>0.046618</td>\n",
       "      <td>-0.040114</td>\n",
       "      <td>-0.146467</td>\n",
       "      <td>0.001091</td>\n",
       "      <td>0.079220</td>\n",
       "      <td>-0.147017</td>\n",
       "      <td>-0.082287</td>\n",
       "      <td>0.037025</td>\n",
       "      <td>-0.001323</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.042859</td>\n",
       "      <td>0.050191</td>\n",
       "      <td>-0.054399</td>\n",
       "      <td>0.070338</td>\n",
       "      <td>-0.018520</td>\n",
       "      <td>0.073124</td>\n",
       "      <td>0.052690</td>\n",
       "      <td>0.103938</td>\n",
       "      <td>0.760666</td>\n",
       "      <td>-0.114571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <td>-0.235955</td>\n",
       "      <td>0.036681</td>\n",
       "      <td>-0.053540</td>\n",
       "      <td>-0.138467</td>\n",
       "      <td>-0.006660</td>\n",
       "      <td>0.072280</td>\n",
       "      <td>-0.175821</td>\n",
       "      <td>-0.095544</td>\n",
       "      <td>0.031095</td>\n",
       "      <td>0.025542</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.048873</td>\n",
       "      <td>0.080018</td>\n",
       "      <td>-0.002026</td>\n",
       "      <td>0.088752</td>\n",
       "      <td>-0.024933</td>\n",
       "      <td>0.068738</td>\n",
       "      <td>0.050930</td>\n",
       "      <td>0.104519</td>\n",
       "      <td>0.940243</td>\n",
       "      <td>-0.080321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <td>-0.052526</td>\n",
       "      <td>-0.057691</td>\n",
       "      <td>0.009827</td>\n",
       "      <td>0.153305</td>\n",
       "      <td>-0.169328</td>\n",
       "      <td>-0.206695</td>\n",
       "      <td>0.032507</td>\n",
       "      <td>-0.028268</td>\n",
       "      <td>0.002727</td>\n",
       "      <td>-0.083745</td>\n",
       "      <td>...</td>\n",
       "      <td>0.420642</td>\n",
       "      <td>0.013044</td>\n",
       "      <td>-0.022057</td>\n",
       "      <td>-0.202716</td>\n",
       "      <td>0.077928</td>\n",
       "      <td>0.012963</td>\n",
       "      <td>-0.034698</td>\n",
       "      <td>-0.132008</td>\n",
       "      <td>-0.104848</td>\n",
       "      <td>-0.057960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous_cancellations</th>\n",
       "      <td>-0.012261</td>\n",
       "      <td>0.008800</td>\n",
       "      <td>0.103606</td>\n",
       "      <td>0.093266</td>\n",
       "      <td>0.074710</td>\n",
       "      <td>-0.003868</td>\n",
       "      <td>0.049290</td>\n",
       "      <td>0.063309</td>\n",
       "      <td>-0.021358</td>\n",
       "      <td>0.110139</td>\n",
       "      <td>...</td>\n",
       "      <td>0.152570</td>\n",
       "      <td>-0.027261</td>\n",
       "      <td>0.005941</td>\n",
       "      <td>-0.059308</td>\n",
       "      <td>-0.018540</td>\n",
       "      <td>-0.048488</td>\n",
       "      <td>-0.027290</td>\n",
       "      <td>-0.020409</td>\n",
       "      <td>-0.015437</td>\n",
       "      <td>0.143306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <td>-0.004467</td>\n",
       "      <td>-0.021971</td>\n",
       "      <td>0.011109</td>\n",
       "      <td>0.091795</td>\n",
       "      <td>-0.117519</td>\n",
       "      <td>-0.142650</td>\n",
       "      <td>0.022533</td>\n",
       "      <td>-0.003205</td>\n",
       "      <td>0.024716</td>\n",
       "      <td>-0.057365</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011963</td>\n",
       "      <td>-0.009416</td>\n",
       "      <td>-0.092905</td>\n",
       "      <td>0.047506</td>\n",
       "      <td>0.037775</td>\n",
       "      <td>-0.022823</td>\n",
       "      <td>-0.099680</td>\n",
       "      <td>-0.053279</td>\n",
       "      <td>-0.031521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>booking_changes</th>\n",
       "      <td>-0.076598</td>\n",
       "      <td>-0.011049</td>\n",
       "      <td>-0.027189</td>\n",
       "      <td>-0.061513</td>\n",
       "      <td>-0.057578</td>\n",
       "      <td>-0.118461</td>\n",
       "      <td>-0.040134</td>\n",
       "      <td>-0.096277</td>\n",
       "      <td>-0.095486</td>\n",
       "      <td>-0.144832</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011963</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.011916</td>\n",
       "      <td>-0.018529</td>\n",
       "      <td>0.067490</td>\n",
       "      <td>0.055003</td>\n",
       "      <td>0.081944</td>\n",
       "      <td>0.006724</td>\n",
       "      <td>0.079407</td>\n",
       "      <td>-0.120657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <td>0.072725</td>\n",
       "      <td>0.029164</td>\n",
       "      <td>-0.031562</td>\n",
       "      <td>0.067054</td>\n",
       "      <td>0.096385</td>\n",
       "      <td>0.048001</td>\n",
       "      <td>0.068336</td>\n",
       "      <td>0.067129</td>\n",
       "      <td>-0.101833</td>\n",
       "      <td>0.054301</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009416</td>\n",
       "      <td>-0.011916</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.014118</td>\n",
       "      <td>-0.030601</td>\n",
       "      <td>-0.082755</td>\n",
       "      <td>-0.036336</td>\n",
       "      <td>-0.026606</td>\n",
       "      <td>-0.022819</td>\n",
       "      <td>0.120329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adr</th>\n",
       "      <td>0.154041</td>\n",
       "      <td>0.228179</td>\n",
       "      <td>-0.019908</td>\n",
       "      <td>-0.160520</td>\n",
       "      <td>-0.004009</td>\n",
       "      <td>0.126516</td>\n",
       "      <td>-0.185932</td>\n",
       "      <td>-0.038347</td>\n",
       "      <td>0.117592</td>\n",
       "      <td>0.081660</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.092905</td>\n",
       "      <td>-0.018529</td>\n",
       "      <td>-0.014118</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.022967</td>\n",
       "      <td>0.111445</td>\n",
       "      <td>0.178729</td>\n",
       "      <td>0.238077</td>\n",
       "      <td>0.093820</td>\n",
       "      <td>-0.015297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <td>-0.218961</td>\n",
       "      <td>-0.021910</td>\n",
       "      <td>0.003455</td>\n",
       "      <td>0.007613</td>\n",
       "      <td>-0.145726</td>\n",
       "      <td>-0.146562</td>\n",
       "      <td>-0.093401</td>\n",
       "      <td>-0.131035</td>\n",
       "      <td>0.061786</td>\n",
       "      <td>-0.195701</td>\n",
       "      <td>...</td>\n",
       "      <td>0.047506</td>\n",
       "      <td>0.067490</td>\n",
       "      <td>-0.030601</td>\n",
       "      <td>0.022967</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.082718</td>\n",
       "      <td>0.069136</td>\n",
       "      <td>0.047829</td>\n",
       "      <td>-0.025871</td>\n",
       "      <td>-0.095092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <td>-0.043478</td>\n",
       "      <td>0.002763</td>\n",
       "      <td>0.006875</td>\n",
       "      <td>-0.200822</td>\n",
       "      <td>-0.155463</td>\n",
       "      <td>0.075047</td>\n",
       "      <td>-0.161406</td>\n",
       "      <td>-0.135337</td>\n",
       "      <td>0.137114</td>\n",
       "      <td>-0.234877</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037775</td>\n",
       "      <td>0.055003</td>\n",
       "      <td>-0.082755</td>\n",
       "      <td>0.111445</td>\n",
       "      <td>0.082718</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.128246</td>\n",
       "      <td>0.157335</td>\n",
       "      <td>0.079966</td>\n",
       "      <td>-0.268305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_family</th>\n",
       "      <td>-0.058094</td>\n",
       "      <td>0.020491</td>\n",
       "      <td>0.001466</td>\n",
       "      <td>-0.044139</td>\n",
       "      <td>-0.104584</td>\n",
       "      <td>-0.026278</td>\n",
       "      <td>-0.133748</td>\n",
       "      <td>-0.179356</td>\n",
       "      <td>0.106618</td>\n",
       "      <td>-0.013226</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.022823</td>\n",
       "      <td>0.081944</td>\n",
       "      <td>-0.036336</td>\n",
       "      <td>0.178729</td>\n",
       "      <td>0.069136</td>\n",
       "      <td>0.128246</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.582024</td>\n",
       "      <td>0.058666</td>\n",
       "      <td>-0.106823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_customer</th>\n",
       "      <td>-0.038762</td>\n",
       "      <td>0.079090</td>\n",
       "      <td>-0.003447</td>\n",
       "      <td>-0.117351</td>\n",
       "      <td>-0.023874</td>\n",
       "      <td>0.087776</td>\n",
       "      <td>-0.246347</td>\n",
       "      <td>-0.229081</td>\n",
       "      <td>0.124318</td>\n",
       "      <td>0.044826</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.099680</td>\n",
       "      <td>0.006724</td>\n",
       "      <td>-0.026606</td>\n",
       "      <td>0.238077</td>\n",
       "      <td>0.047829</td>\n",
       "      <td>0.157335</td>\n",
       "      <td>0.582024</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.118753</td>\n",
       "      <td>-0.082684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_nights</th>\n",
       "      <td>-0.249747</td>\n",
       "      <td>0.045648</td>\n",
       "      <td>-0.055689</td>\n",
       "      <td>-0.160759</td>\n",
       "      <td>-0.004546</td>\n",
       "      <td>0.084999</td>\n",
       "      <td>-0.188866</td>\n",
       "      <td>-0.103571</td>\n",
       "      <td>0.037720</td>\n",
       "      <td>0.018554</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.053279</td>\n",
       "      <td>0.079407</td>\n",
       "      <td>-0.022819</td>\n",
       "      <td>0.093820</td>\n",
       "      <td>-0.025871</td>\n",
       "      <td>0.079966</td>\n",
       "      <td>0.058666</td>\n",
       "      <td>0.118753</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.104849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deposit_given</th>\n",
       "      <td>0.172415</td>\n",
       "      <td>0.057361</td>\n",
       "      <td>0.047597</td>\n",
       "      <td>0.395685</td>\n",
       "      <td>0.397229</td>\n",
       "      <td>0.115160</td>\n",
       "      <td>0.201923</td>\n",
       "      <td>0.263383</td>\n",
       "      <td>0.121789</td>\n",
       "      <td>0.481507</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.031521</td>\n",
       "      <td>-0.120657</td>\n",
       "      <td>0.120329</td>\n",
       "      <td>-0.015297</td>\n",
       "      <td>-0.095092</td>\n",
       "      <td>-0.268305</td>\n",
       "      <td>-0.106823</td>\n",
       "      <td>-0.082684</td>\n",
       "      <td>-0.104849</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   hotel  arrival_date_month      meal  \\\n",
       "hotel                           1.000000            0.051197  0.061782   \n",
       "arrival_date_month              0.051197            1.000000 -0.010208   \n",
       "meal                            0.061782           -0.010208  1.000000   \n",
       "country                        -0.040609           -0.052405  0.022476   \n",
       "market_segment                  0.102592            0.047902 -0.026007   \n",
       "distribution_channel            0.182266            0.077075 -0.025486   \n",
       "reserved_room_type              0.198171           -0.038801  0.010035   \n",
       "assigned_room_type              0.280884            0.015456  0.013913   \n",
       "customer_type                  -0.008378           -0.029636  0.105767   \n",
       "reservation_status              0.137082            0.069886  0.050584   \n",
       "reservation_status_date         0.107481           -0.089512  0.038298   \n",
       "is_canceled                     0.137082            0.069886  0.050584   \n",
       "lead_time                       0.109869            0.241413 -0.040315   \n",
       "arrival_date_year               0.035176            0.015509 -0.024568   \n",
       "arrival_date_week_number        0.001241            0.155653  0.015308   \n",
       "arrival_date_day_of_month      -0.001678            0.002248 -0.005737   \n",
       "stays_in_weekend_nights        -0.187816            0.046618 -0.040114   \n",
       "stays_in_week_nights           -0.235955            0.036681 -0.053540   \n",
       "is_repeated_guest              -0.052526           -0.057691  0.009827   \n",
       "previous_cancellations         -0.012261            0.008800  0.103606   \n",
       "previous_bookings_not_canceled -0.004467           -0.021971  0.011109   \n",
       "booking_changes                -0.076598           -0.011049 -0.027189   \n",
       "days_in_waiting_list            0.072725            0.029164 -0.031562   \n",
       "adr                             0.154041            0.228179 -0.019908   \n",
       "required_car_parking_spaces    -0.218961           -0.021910  0.003455   \n",
       "total_of_special_requests      -0.043478            0.002763  0.006875   \n",
       "is_family                      -0.058094            0.020491  0.001466   \n",
       "total_customer                 -0.038762            0.079090 -0.003447   \n",
       "total_nights                   -0.249747            0.045648 -0.055689   \n",
       "deposit_given                   0.172415            0.057361  0.047597   \n",
       "\n",
       "                                 country  market_segment  \\\n",
       "hotel                          -0.040609        0.102592   \n",
       "arrival_date_month             -0.052405        0.047902   \n",
       "meal                            0.022476       -0.026007   \n",
       "country                         1.000000        0.078982   \n",
       "market_segment                  0.078982        1.000000   \n",
       "distribution_channel           -0.129774        0.540116   \n",
       "reserved_room_type              0.153177        0.182813   \n",
       "assigned_room_type              0.125415        0.209272   \n",
       "customer_type                  -0.029320       -0.265606   \n",
       "reservation_status              0.357232        0.267006   \n",
       "reservation_status_date         0.340400        0.276512   \n",
       "is_canceled                     0.357232        0.267006   \n",
       "lead_time                      -0.071919        0.397346   \n",
       "arrival_date_year              -0.180684       -0.064498   \n",
       "arrival_date_week_number        0.017322        0.009895   \n",
       "arrival_date_day_of_month       0.001898        0.008062   \n",
       "stays_in_weekend_nights        -0.146467        0.001091   \n",
       "stays_in_week_nights           -0.138467       -0.006660   \n",
       "is_repeated_guest               0.153305       -0.169328   \n",
       "previous_cancellations          0.093266        0.074710   \n",
       "previous_bookings_not_canceled  0.091795       -0.117519   \n",
       "booking_changes                -0.061513       -0.057578   \n",
       "days_in_waiting_list            0.067054        0.096385   \n",
       "adr                            -0.160520       -0.004009   \n",
       "required_car_parking_spaces     0.007613       -0.145726   \n",
       "total_of_special_requests      -0.200822       -0.155463   \n",
       "is_family                      -0.044139       -0.104584   \n",
       "total_customer                 -0.117351       -0.023874   \n",
       "total_nights                   -0.160759       -0.004546   \n",
       "deposit_given                   0.395685        0.397229   \n",
       "\n",
       "                                distribution_channel  reserved_room_type  \\\n",
       "hotel                                       0.182266            0.198171   \n",
       "arrival_date_month                          0.077075           -0.038801   \n",
       "meal                                       -0.025486            0.010035   \n",
       "country                                    -0.129774            0.153177   \n",
       "market_segment                              0.540116            0.182813   \n",
       "distribution_channel                        1.000000            0.070381   \n",
       "reserved_room_type                          0.070381            1.000000   \n",
       "assigned_room_type                          0.118970            0.722724   \n",
       "customer_type                              -0.023234           -0.143390   \n",
       "reservation_status                          0.177167            0.072769   \n",
       "reservation_status_date                     0.081435            0.148830   \n",
       "is_canceled                                 0.177167            0.072769   \n",
       "lead_time                                   0.332215            0.026146   \n",
       "arrival_date_year                           0.013924           -0.092503   \n",
       "arrival_date_week_number                    0.010940            0.010119   \n",
       "arrival_date_day_of_month                  -0.003948           -0.016947   \n",
       "stays_in_weekend_nights                     0.079220           -0.147017   \n",
       "stays_in_week_nights                        0.072280           -0.175821   \n",
       "is_repeated_guest                          -0.206695            0.032507   \n",
       "previous_cancellations                     -0.003868            0.049290   \n",
       "previous_bookings_not_canceled             -0.142650            0.022533   \n",
       "booking_changes                            -0.118461           -0.040134   \n",
       "days_in_waiting_list                        0.048001            0.068336   \n",
       "adr                                         0.126516           -0.185932   \n",
       "required_car_parking_spaces                -0.146562           -0.093401   \n",
       "total_of_special_requests                   0.075047           -0.161406   \n",
       "is_family                                  -0.026278           -0.133748   \n",
       "total_customer                              0.087776           -0.246347   \n",
       "total_nights                                0.084999           -0.188866   \n",
       "deposit_given                               0.115160            0.201923   \n",
       "\n",
       "                                assigned_room_type  customer_type  \\\n",
       "hotel                                     0.280884      -0.008378   \n",
       "arrival_date_month                        0.015456      -0.029636   \n",
       "meal                                      0.013913       0.105767   \n",
       "country                                   0.125415      -0.029320   \n",
       "market_segment                            0.209272      -0.265606   \n",
       "distribution_channel                      0.118970      -0.023234   \n",
       "reserved_room_type                        0.722724      -0.143390   \n",
       "assigned_room_type                        1.000000      -0.084025   \n",
       "customer_type                            -0.084025       1.000000   \n",
       "reservation_status                        0.201570       0.136617   \n",
       "reservation_status_date                   0.191989       0.055937   \n",
       "is_canceled                               0.201570       0.136617   \n",
       "lead_time                                 0.142173      -0.135881   \n",
       "arrival_date_year                        -0.025296       0.204725   \n",
       "arrival_date_week_number                  0.005852      -0.067980   \n",
       "arrival_date_day_of_month                -0.011957      -0.002012   \n",
       "stays_in_weekend_nights                  -0.082287       0.037025   \n",
       "stays_in_week_nights                     -0.095544       0.031095   \n",
       "is_repeated_guest                        -0.028268       0.002727   \n",
       "previous_cancellations                    0.063309      -0.021358   \n",
       "previous_bookings_not_canceled           -0.003205       0.024716   \n",
       "booking_changes                          -0.096277      -0.095486   \n",
       "days_in_waiting_list                      0.067129      -0.101833   \n",
       "adr                                      -0.038347       0.117592   \n",
       "required_car_parking_spaces              -0.131035       0.061786   \n",
       "total_of_special_requests                -0.135337       0.137114   \n",
       "is_family                                -0.179356       0.106618   \n",
       "total_customer                           -0.229081       0.124318   \n",
       "total_nights                             -0.103571       0.037720   \n",
       "deposit_given                             0.263383       0.121789   \n",
       "\n",
       "                                reservation_status  ...  \\\n",
       "hotel                                     0.137082  ...   \n",
       "arrival_date_month                        0.069886  ...   \n",
       "meal                                      0.050584  ...   \n",
       "country                                   0.357232  ...   \n",
       "market_segment                            0.267006  ...   \n",
       "distribution_channel                      0.177167  ...   \n",
       "reserved_room_type                        0.072769  ...   \n",
       "assigned_room_type                        0.201570  ...   \n",
       "customer_type                             0.136617  ...   \n",
       "reservation_status                        1.000000  ...   \n",
       "reservation_status_date                   0.488307  ...   \n",
       "is_canceled                               1.000000  ...   \n",
       "lead_time                                 0.320075  ...   \n",
       "arrival_date_year                         0.016622  ...   \n",
       "arrival_date_week_number                  0.008315  ...   \n",
       "arrival_date_day_of_month                -0.005948  ...   \n",
       "stays_in_weekend_nights                  -0.001323  ...   \n",
       "stays_in_week_nights                      0.025542  ...   \n",
       "is_repeated_guest                        -0.083745  ...   \n",
       "previous_cancellations                    0.110139  ...   \n",
       "previous_bookings_not_canceled           -0.057365  ...   \n",
       "booking_changes                          -0.144832  ...   \n",
       "days_in_waiting_list                      0.054301  ...   \n",
       "adr                                       0.081660  ...   \n",
       "required_car_parking_spaces              -0.195701  ...   \n",
       "total_of_special_requests                -0.234877  ...   \n",
       "is_family                                -0.013226  ...   \n",
       "total_customer                            0.044826  ...   \n",
       "total_nights                              0.018554  ...   \n",
       "deposit_given                             0.481507  ...   \n",
       "\n",
       "                                previous_bookings_not_canceled  \\\n",
       "hotel                                                -0.004467   \n",
       "arrival_date_month                                   -0.021971   \n",
       "meal                                                  0.011109   \n",
       "country                                               0.091795   \n",
       "market_segment                                       -0.117519   \n",
       "distribution_channel                                 -0.142650   \n",
       "reserved_room_type                                    0.022533   \n",
       "assigned_room_type                                   -0.003205   \n",
       "customer_type                                         0.024716   \n",
       "reservation_status                                   -0.057365   \n",
       "reservation_status_date                              -0.007066   \n",
       "is_canceled                                          -0.057365   \n",
       "lead_time                                            -0.121761   \n",
       "arrival_date_year                                     0.029234   \n",
       "arrival_date_week_number                             -0.021009   \n",
       "arrival_date_day_of_month                            -0.000306   \n",
       "stays_in_weekend_nights                              -0.042859   \n",
       "stays_in_week_nights                                 -0.048873   \n",
       "is_repeated_guest                                     0.420642   \n",
       "previous_cancellations                                0.152570   \n",
       "previous_bookings_not_canceled                        1.000000   \n",
       "booking_changes                                       0.011963   \n",
       "days_in_waiting_list                                 -0.009416   \n",
       "adr                                                  -0.092905   \n",
       "required_car_parking_spaces                           0.047506   \n",
       "total_of_special_requests                             0.037775   \n",
       "is_family                                            -0.022823   \n",
       "total_customer                                       -0.099680   \n",
       "total_nights                                         -0.053279   \n",
       "deposit_given                                        -0.031521   \n",
       "\n",
       "                                booking_changes  days_in_waiting_list  \\\n",
       "hotel                                 -0.076598              0.072725   \n",
       "arrival_date_month                    -0.011049              0.029164   \n",
       "meal                                  -0.027189             -0.031562   \n",
       "country                               -0.061513              0.067054   \n",
       "market_segment                        -0.057578              0.096385   \n",
       "distribution_channel                  -0.118461              0.048001   \n",
       "reserved_room_type                    -0.040134              0.068336   \n",
       "assigned_room_type                    -0.096277              0.067129   \n",
       "customer_type                         -0.095486             -0.101833   \n",
       "reservation_status                    -0.144832              0.054301   \n",
       "reservation_status_date               -0.111565              0.042947   \n",
       "is_canceled                           -0.144832              0.054301   \n",
       "lead_time                              0.006506              0.124403   \n",
       "arrival_date_year                      0.031416             -0.056348   \n",
       "arrival_date_week_number               0.006311              0.022677   \n",
       "arrival_date_day_of_month              0.011266              0.022532   \n",
       "stays_in_weekend_nights                0.050191             -0.054399   \n",
       "stays_in_week_nights                   0.080018             -0.002026   \n",
       "is_repeated_guest                      0.013044             -0.022057   \n",
       "previous_cancellations                -0.027261              0.005941   \n",
       "previous_bookings_not_canceled         0.011963             -0.009416   \n",
       "booking_changes                        1.000000             -0.011916   \n",
       "days_in_waiting_list                  -0.011916              1.000000   \n",
       "adr                                   -0.018529             -0.014118   \n",
       "required_car_parking_spaces            0.067490             -0.030601   \n",
       "total_of_special_requests              0.055003             -0.082755   \n",
       "is_family                              0.081944             -0.036336   \n",
       "total_customer                         0.006724             -0.026606   \n",
       "total_nights                           0.079407             -0.022819   \n",
       "deposit_given                         -0.120657              0.120329   \n",
       "\n",
       "                                     adr  required_car_parking_spaces  \\\n",
       "hotel                           0.154041                    -0.218961   \n",
       "arrival_date_month              0.228179                    -0.021910   \n",
       "meal                           -0.019908                     0.003455   \n",
       "country                        -0.160520                     0.007613   \n",
       "market_segment                 -0.004009                    -0.145726   \n",
       "distribution_channel            0.126516                    -0.146562   \n",
       "reserved_room_type             -0.185932                    -0.093401   \n",
       "assigned_room_type             -0.038347                    -0.131035   \n",
       "customer_type                   0.117592                     0.061786   \n",
       "reservation_status              0.081660                    -0.195701   \n",
       "reservation_status_date        -0.106156                    -0.087884   \n",
       "is_canceled                     0.081660                    -0.195701   \n",
       "lead_time                       0.139263                    -0.148886   \n",
       "arrival_date_year               0.160043                    -0.013812   \n",
       "arrival_date_week_number        0.036842                     0.001980   \n",
       "arrival_date_day_of_month       0.021400                     0.008569   \n",
       "stays_in_weekend_nights         0.070338                    -0.018520   \n",
       "stays_in_week_nights            0.088752                    -0.024933   \n",
       "is_repeated_guest              -0.202716                     0.077928   \n",
       "previous_cancellations         -0.059308                    -0.018540   \n",
       "previous_bookings_not_canceled -0.092905                     0.047506   \n",
       "booking_changes                -0.018529                     0.067490   \n",
       "days_in_waiting_list           -0.014118                    -0.030601   \n",
       "adr                             1.000000                     0.022967   \n",
       "required_car_parking_spaces     0.022967                     1.000000   \n",
       "total_of_special_requests       0.111445                     0.082718   \n",
       "is_family                       0.178729                     0.069136   \n",
       "total_customer                  0.238077                     0.047829   \n",
       "total_nights                    0.093820                    -0.025871   \n",
       "deposit_given                  -0.015297                    -0.095092   \n",
       "\n",
       "                                total_of_special_requests  is_family  \\\n",
       "hotel                                           -0.043478  -0.058094   \n",
       "arrival_date_month                               0.002763   0.020491   \n",
       "meal                                             0.006875   0.001466   \n",
       "country                                         -0.200822  -0.044139   \n",
       "market_segment                                  -0.155463  -0.104584   \n",
       "distribution_channel                             0.075047  -0.026278   \n",
       "reserved_room_type                              -0.161406  -0.133748   \n",
       "assigned_room_type                              -0.135337  -0.179356   \n",
       "customer_type                                    0.137114   0.106618   \n",
       "reservation_status                              -0.234877  -0.013226   \n",
       "reservation_status_date                         -0.218839  -0.084130   \n",
       "is_canceled                                     -0.234877  -0.013226   \n",
       "lead_time                                       -0.029317  -0.009693   \n",
       "arrival_date_year                                0.108610   0.052787   \n",
       "arrival_date_week_number                         0.026202   0.010661   \n",
       "arrival_date_day_of_month                        0.003026   0.014706   \n",
       "stays_in_weekend_nights                          0.073124   0.052690   \n",
       "stays_in_week_nights                             0.068738   0.050930   \n",
       "is_repeated_guest                                0.012963  -0.034698   \n",
       "previous_cancellations                          -0.048488  -0.027290   \n",
       "previous_bookings_not_canceled                   0.037775  -0.022823   \n",
       "booking_changes                                  0.055003   0.081944   \n",
       "days_in_waiting_list                            -0.082755  -0.036336   \n",
       "adr                                              0.111445   0.178729   \n",
       "required_car_parking_spaces                      0.082718   0.069136   \n",
       "total_of_special_requests                        1.000000   0.128246   \n",
       "is_family                                        0.128246   1.000000   \n",
       "total_customer                                   0.157335   0.582024   \n",
       "total_nights                                     0.079966   0.058666   \n",
       "deposit_given                                   -0.268305  -0.106823   \n",
       "\n",
       "                                total_customer  total_nights  deposit_given  \n",
       "hotel                                -0.038762     -0.249747       0.172415  \n",
       "arrival_date_month                    0.079090      0.045648       0.057361  \n",
       "meal                                 -0.003447     -0.055689       0.047597  \n",
       "country                              -0.117351     -0.160759       0.395685  \n",
       "market_segment                       -0.023874     -0.004546       0.397229  \n",
       "distribution_channel                  0.087776      0.084999       0.115160  \n",
       "reserved_room_type                   -0.246347     -0.188866       0.201923  \n",
       "assigned_room_type                   -0.229081     -0.103571       0.263383  \n",
       "customer_type                         0.124318      0.037720       0.121789  \n",
       "reservation_status                    0.044826      0.018554       0.481507  \n",
       "reservation_status_date              -0.057482     -0.063224       0.555202  \n",
       "is_canceled                           0.044826      0.018554       0.481507  \n",
       "lead_time                             0.136837      0.277977       0.292830  \n",
       "arrival_date_year                     0.052780      0.031796      -0.065975  \n",
       "arrival_date_week_number              0.025752      0.019279       0.007831  \n",
       "arrival_date_day_of_month             0.006637     -0.027533      -0.008643  \n",
       "stays_in_weekend_nights               0.103938      0.760666      -0.114571  \n",
       "stays_in_week_nights                  0.104519      0.940243      -0.080321  \n",
       "is_repeated_guest                    -0.132008     -0.104848      -0.057960  \n",
       "previous_cancellations               -0.020409     -0.015437       0.143306  \n",
       "previous_bookings_not_canceled       -0.099680     -0.053279      -0.031521  \n",
       "booking_changes                       0.006724      0.079407      -0.120657  \n",
       "days_in_waiting_list                 -0.026606     -0.022819       0.120329  \n",
       "adr                                   0.238077      0.093820      -0.015297  \n",
       "required_car_parking_spaces           0.047829     -0.025871      -0.095092  \n",
       "total_of_special_requests             0.157335      0.079966      -0.268305  \n",
       "is_family                             0.582024      0.058666      -0.106823  \n",
       "total_customer                        1.000000      0.118753      -0.082684  \n",
       "total_nights                          0.118753      1.000000      -0.104849  \n",
       "deposit_given                        -0.082684     -0.104849       1.000000  \n",
       "\n",
       "[30 rows x 30 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "2c4c8b12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reservation_status                1.000000\n",
       "is_canceled                       1.000000\n",
       "reservation_status_date           0.488307\n",
       "deposit_given                     0.481507\n",
       "country                           0.357232\n",
       "lead_time                         0.320075\n",
       "market_segment                    0.267006\n",
       "assigned_room_type                0.201570\n",
       "distribution_channel              0.177167\n",
       "hotel                             0.137082\n",
       "customer_type                     0.136617\n",
       "previous_cancellations            0.110139\n",
       "adr                               0.081660\n",
       "reserved_room_type                0.072769\n",
       "arrival_date_month                0.069886\n",
       "days_in_waiting_list              0.054301\n",
       "meal                              0.050584\n",
       "total_customer                    0.044826\n",
       "stays_in_week_nights              0.025542\n",
       "total_nights                      0.018554\n",
       "arrival_date_year                 0.016622\n",
       "arrival_date_week_number          0.008315\n",
       "stays_in_weekend_nights          -0.001323\n",
       "arrival_date_day_of_month        -0.005948\n",
       "is_family                        -0.013226\n",
       "previous_bookings_not_canceled   -0.057365\n",
       "is_repeated_guest                -0.083745\n",
       "booking_changes                  -0.144832\n",
       "required_car_parking_spaces      -0.195701\n",
       "total_of_special_requests        -0.234877\n",
       "Name: is_canceled, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr['is_canceled'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "2c127b64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['reservation_status', 'is_canceled', 'reservation_status_date',\n",
       "       'deposit_given', 'country', 'lead_time', 'market_segment',\n",
       "       'assigned_room_type', 'distribution_channel', 'hotel', 'customer_type',\n",
       "       'previous_cancellations', 'adr', 'reserved_room_type',\n",
       "       'arrival_date_month', 'days_in_waiting_list', 'meal', 'total_customer',\n",
       "       'stays_in_week_nights', 'total_nights', 'arrival_date_year',\n",
       "       'arrival_date_week_number', 'stays_in_weekend_nights',\n",
       "       'arrival_date_day_of_month', 'is_family',\n",
       "       'previous_bookings_not_canceled', 'is_repeated_guest',\n",
       "       'booking_changes', 'required_car_parking_spaces',\n",
       "       'total_of_special_requests'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr['is_canceled'].sort_values(ascending=False).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "7479b142",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_drop=['reservation_status', 'reservation_status_date','arrival_date_year',\n",
    "       'arrival_date_week_number', 'stays_in_weekend_nights',\n",
    "       'arrival_date_day_of_month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "b53c71db",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe.drop(features_to_drop,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "c7237a9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(119210, 24)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ee38002",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e779f3c0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a941110",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "77f6f931",
   "metadata": {},
   "source": [
    "# 12.. How to find Important features for model building.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "47929dbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>customer_type</th>\n",
       "      <th>is_canceled</th>\n",
       "      <th>...</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>is_family</th>\n",
       "      <th>total_customer</th>\n",
       "      <th>total_nights</th>\n",
       "      <th>deposit_given</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.277674</td>\n",
       "      <td>0.305016</td>\n",
       "      <td>0.374106</td>\n",
       "      <td>0.379365</td>\n",
       "      <td>0.36759</td>\n",
       "      <td>0.410598</td>\n",
       "      <td>0.391567</td>\n",
       "      <td>0.251373</td>\n",
       "      <td>0.254504</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.109890</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.277674</td>\n",
       "      <td>0.305016</td>\n",
       "      <td>0.374106</td>\n",
       "      <td>0.379365</td>\n",
       "      <td>0.36759</td>\n",
       "      <td>0.410598</td>\n",
       "      <td>0.407654</td>\n",
       "      <td>0.352528</td>\n",
       "      <td>0.254504</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.763028</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      hotel  arrival_date_month      meal   country  market_segment  \\\n",
       "0  0.277674            0.305016  0.374106  0.379365         0.36759   \n",
       "1  0.277674            0.305016  0.374106  0.379365         0.36759   \n",
       "\n",
       "   distribution_channel  reserved_room_type  assigned_room_type  \\\n",
       "0              0.410598            0.391567            0.251373   \n",
       "1              0.410598            0.407654            0.352528   \n",
       "\n",
       "   customer_type  is_canceled  ...  previous_bookings_not_canceled  \\\n",
       "0       0.254504            0  ...                               0   \n",
       "1       0.254504            0  ...                               0   \n",
       "\n",
       "   booking_changes  days_in_waiting_list       adr  \\\n",
       "0                0                     0  4.109890   \n",
       "1                0                     0  4.763028   \n",
       "\n",
       "   required_car_parking_spaces  total_of_special_requests  is_family  \\\n",
       "0                            0                          1          0   \n",
       "1                            1                          1          1   \n",
       "\n",
       "   total_customer  total_nights  deposit_given  \n",
       "0             2.0             1              0  \n",
       "1             4.0             1              0  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "75d1c52d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hotel                             0\n",
       "arrival_date_month                0\n",
       "meal                              0\n",
       "country                           0\n",
       "market_segment                    0\n",
       "distribution_channel              0\n",
       "reserved_room_type                0\n",
       "assigned_room_type                0\n",
       "customer_type                     0\n",
       "is_canceled                       0\n",
       "lead_time                         0\n",
       "stays_in_week_nights              0\n",
       "is_repeated_guest                 0\n",
       "previous_cancellations            0\n",
       "previous_bookings_not_canceled    0\n",
       "booking_changes                   0\n",
       "days_in_waiting_list              0\n",
       "adr                               1\n",
       "required_car_parking_spaces       0\n",
       "total_of_special_requests         0\n",
       "is_family                         0\n",
       "total_customer                    0\n",
       "total_nights                      0\n",
       "deposit_given                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "37b02b69",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "849d6a63",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "e258a0c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "## separate dependent & independent features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "18f94fa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=dataframe.drop('is_canceled',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "fd1c563a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=dataframe['is_canceled']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "7d58ebb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in c:\\users\\dell\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (1.2.2)"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip available: 22.3.1 -> 23.1.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\dell\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from scikit-learn) (1.23.5)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\dell\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from scikit-learn) (1.10.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\dell\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\dell\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from scikit-learn) (3.1.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install -U scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "de85e748",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.feature_selection import SelectFromModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "76129a84",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Lasso(alpha=0.005)\n",
    "# select a suitable alpha (equivalent of penalty).\n",
    "# The bigger the alpha the less features that will be selected.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "7390fc6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_sel_model=SelectFromModel(Lasso(alpha=0.005))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "eddbdfed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SelectFromModel(estimator=Lasso(alpha=0.005))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectFromModel</label><div class=\"sk-toggleable__content\"><pre>SelectFromModel(estimator=Lasso(alpha=0.005))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=0.005)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=0.005)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "SelectFromModel(estimator=Lasso(alpha=0.005))"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_sel_model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "140d5c36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False,  True, False, False, False, False, False,\n",
       "        True, False, False,  True,  True,  True,  True,  True,  True,\n",
       "        True, False,  True,  True,  True])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_sel_model.get_support()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "d54d2a81",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "a4da036f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['hotel', 'arrival_date_month', 'meal', 'country', 'market_segment',\n",
       "       'distribution_channel', 'reserved_room_type', 'assigned_room_type',\n",
       "       'customer_type', 'lead_time', 'stays_in_week_nights',\n",
       "       'is_repeated_guest', 'previous_cancellations',\n",
       "       'previous_bookings_not_canceled', 'booking_changes',\n",
       "       'days_in_waiting_list', 'adr', 'required_car_parking_spaces',\n",
       "       'total_of_special_requests', 'is_family', 'total_customer',\n",
       "       'total_nights', 'deposit_given'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "68e57c54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's print the number of selected features\n",
    "\n",
    "selected_feature=cols[feature_sel_model.get_support()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "671736b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'lead_time', 'previous_cancellations',\n",
       "       'previous_bookings_not_canceled', 'booking_changes',\n",
       "       'days_in_waiting_list', 'adr', 'required_car_parking_spaces',\n",
       "       'total_of_special_requests', 'total_customer', 'total_nights',\n",
       "       'deposit_given'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "c7d8e530",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=x[selected_feature]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "ac618d67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0\n",
       "1         0\n",
       "2         1\n",
       "3         0\n",
       "4         1\n",
       "         ..\n",
       "119205    0\n",
       "119206    0\n",
       "119207    0\n",
       "119208    0\n",
       "119209    0\n",
       "Name: is_canceled, Length: 119209, dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9858123a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5552b2c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "820a7090",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afcbb605",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "32c74b34",
   "metadata": {},
   "source": [
    "# 13.. Lets build ML model.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "a910de96",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "b677f91f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( x, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "656a4591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(89406, 12)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "2337c837",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "a4bf12ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "ce1942e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "dbf3e4eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "27939de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "a62c97dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "c6bb809c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[17419,  1247],\n",
       "       [ 4799,  6338]], dtype=int64)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "bd2b7ec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "02ee4827",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7971345166593967"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e797dfb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a47f9e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcf62856",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "521e4974",
   "metadata": {},
   "source": [
    "# 14.. How to cross-validate model.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "1d69ae06",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "7bc6050a",
   "metadata": {},
   "outputs": [],
   "source": [
    "score=cross_val_score(logreg,x,y,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "2dc136ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.77854207, 0.78424629, 0.78240081, 0.8144451 , 0.81494841,\n",
       "       0.7393675 , 0.74104521, 0.8557168 , 0.83063501, 0.80436242])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "b8abe96d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7945709618523287"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "474aed74",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "754271e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7f2f469",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "630322a3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4c338c68",
   "metadata": {},
   "source": [
    "# 15.. playing with multiple algos.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "67afc867",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "5546b1a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "models=[]\n",
    "\n",
    "models.append(('LogisticRegression',LogisticRegression()))\n",
    "models.append(('Naive_bayes',GaussianNB()))\n",
    "models.append(('Random Forest',RandomForestClassifier()))\n",
    "models.append(('Decision_tree',DecisionTreeClassifier()))\n",
    "models.append(('KNN',KNeighborsClassifier()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "5f1433c1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression\n",
      "[[17419  4799]\n",
      " [ 1247  6338]]\n",
      "0.7971345166593967\n",
      "\n",
      "\n",
      "Naive_bayes\n",
      "[[ 6637   700]\n",
      " [12029 10437]]\n",
      "0.5728953461060967\n",
      "\n",
      "\n",
      "Random Forest\n",
      "[[17088  2796]\n",
      " [ 1578  8341]]\n",
      "0.8532362513840889\n",
      "\n",
      "\n",
      "Decision_tree\n",
      "[[15854  2685]\n",
      " [ 2812  8452]]\n",
      "0.8155554809918465\n",
      "\n",
      "\n",
      "KNN\n",
      "[[16670  3345]\n",
      " [ 1996  7792]]\n",
      "0.8207898533704661\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for name,model in models:\n",
    "    print(name)\n",
    "    model.fit(X_train,y_train)\n",
    "    \n",
    "    predictions=model.predict(X_test)\n",
    "    \n",
    "    from sklearn.metrics import confusion_matrix\n",
    "    cm=confusion_matrix(predictions,y_test)\n",
    "    print(cm)\n",
    "    \n",
    "    from sklearn.metrics import accuracy_score\n",
    "    acc=accuracy_score(predictions,y_test)\n",
    "    print(acc)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d61cd4a",
   "metadata": {},
   "source": [
    "Result - Random forest gives the highest accuracy eqaul to 85.75%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6dec76c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4af0793",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c897a113",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "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": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}