
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline


df=pd.read_csv(r'C:\Users\yunge\Desktop\Data Science\Python\Data Analysis Projects\Hotel Booking Analysis\hotel_bookings.csv')
df.head()


df.shape

(119390, 32)


df.isnull().values.any()

True


df.isnull().sum()

hotel                                  0
is_canceled                            0
lead_time                              0
arrival_date_year                      0
arrival_date_month                     0
arrival_date_week_number               0
arrival_date_day_of_month              0
stays_in_weekend_nights                0
stays_in_week_nights                   0
adults                                 0
children                               4
babies                                 0
meal                                   0
country                              488
market_segment                         0
distribution_channel                   0
is_repeated_guest                      0
previous_cancellations                 0
previous_bookings_not_canceled         0
reserved_room_type                     0
assigned_room_type                     0
booking_changes                        0
deposit_type                           0
agent                              16340
company                           112593
days_in_waiting_list                   0
customer_type                          0
adr                                    0
required_car_parking_spaces            0
total_of_special_requests              0
reservation_status                     0
reservation_status_date                0
dtype: int64


# Fill the null values with 0
df.fillna(0,inplace=True)


df.isna().sum()

hotel                             0
is_canceled                       0
lead_time                         0
arrival_date_year                 0
arrival_date_month                0
arrival_date_week_number          0
arrival_date_day_of_month         0
stays_in_weekend_nights           0
stays_in_week_nights              0
adults                            0
children                          0
babies                            0
meal                              0
country                           0
market_segment                    0
distribution_channel              0
is_repeated_guest                 0
previous_cancellations            0
previous_bookings_not_canceled    0
reserved_room_type                0
assigned_room_type                0
booking_changes                   0
deposit_type                      0
agent                             0
company                           0
days_in_waiting_list              0
customer_type                     0
adr                               0
required_car_parking_spaces       0
total_of_special_requests         0
reservation_status                0
reservation_status_date           0
dtype: int64


df['meal'].value_counts()

BB           92310
HB           14463
SC           10650
Undefined     1169
FB             798
Name: meal, dtype: int64


df['meal'].unique()

array(['BB', 'FB', 'HB', 'SC', 'Undefined'], dtype=object)


df['adults'].unique()

array([ 2,  1,  3,  4, 40, 26, 50, 27, 55,  0, 20,  6,  5, 10],
      dtype=int64)


df['children'].unique()

array([ 0.,  1.,  2., 10.,  3.])


df['babies'].unique()

array([ 0,  1,  2, 10,  9], dtype=int64)


# There are 180 rows which have 0 as entry for all 3 columns 'adults, children and babies', indicating these are wrong entries as there are no guest who checked into the hotel 
filter=(df['adults']==0) & (df['children']==0) & (df['babies']==0)
df[filter]


# There are 119,210 rows left after calling the filter; ~ as negation
df[~filter]


# This will be our processed data and we will be using this dataset for further analysis
data=df[~filter]


data.shape

(119210, 32)


df['hotel'].unique()

array(['Resort Hotel', 'City Hotel'], dtype=object)


df['is_canceled'].unique()

array([0, 1], dtype=int64)


resort=data[(data['hotel']=='Resort Hotel') & (data['is_canceled']==0)]
city=data[(data['hotel']=='City Hotel') & (data['is_canceled']==0)]


resort.head()


city.head()


resort.shape

(28927, 32)


city.shape

(46084, 32)


pip install plotly

Requirement already satisfied: plotly in c:\users\yunge\anaconda3\lib\site-packages (5.1.0)
Requirement already satisfied: six in c:\users\yunge\anaconda3\lib\site-packages (from plotly) (1.15.0)
Requirement already satisfied: tenacity>=6.2.0 in c:\users\yunge\anaconda3\lib\site-packages (from plotly) (8.0.1)
Note: you may need to restart the kernel to use updated packages.


import plotly.graph_objs as go
from plotly.offline import iplot
import plotly.express as px


labels=resort['country'].value_counts().index
values=resort['country'].value_counts()


fig=px.pie(values=values,
             names=labels,
             title="Home Country of Guests for Resort Hotel",
             template="seaborn")

fig.update_traces(textposition="inside", textinfo="value+percent+label")
fig.show()


labels2=city['country'].value_counts().index
values2=city['country'].value_counts()

fig=px.pie(values=values2,
             names=labels2,
             title="Home Country of Guests for City Hotel",
             template="seaborn")

fig.update_traces(textposition="inside", textinfo="value+percent+label")
fig.show()


# A dataframe where is_canceled equals 0, including both resort hotel and city hotel
data[data['is_canceled']==0]


# Currently country used as index
data[data['is_canceled']==0]['country'].value_counts()

PRT    20977
GBR     9668
FRA     8468
ESP     6383
DEU     6067
       ...  
ZMB        1
GUY        1
BWA        1
MRT        1
BHR        1
Name: country, Length: 166, dtype: int64


# Reset index
country_data=data[data['is_canceled']==0]['country'].value_counts().reset_index()
country_data.head()


# Renaming columns of country_data 
country_data.columns=['Country','Number of Guests']
country_data.head()


px.choropleth(country_data,
              locations=country_data['Country'],
              color=country_data['Number of Guests'],
              hover_name=country_data['Country'],
              title='Home Country of Guests')


data.shape

(119210, 32)


data.head()


data.columns

Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',
       'arrival_date_month', 'arrival_date_week_number',
       'arrival_date_day_of_month', 'stays_in_weekend_nights',
       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',
       'country', 'market_segment', 'distribution_channel',
       'is_repeated_guest', 'previous_cancellations',
       'previous_bookings_not_canceled', 'reserved_room_type',
       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',
       'company', 'days_in_waiting_list', 'customer_type', 'adr',
       'required_car_parking_spaces', 'total_of_special_requests',
       'reservation_status', 'reservation_status_date'],
      dtype='object')


data2=data[data['is_canceled']==0]


# ADR as average daily rate 
plt.figure(figsize=(12,8))
sns.boxplot(x='reserved_room_type',y='adr',data=data2,hue='hotel') 
plt.title('Price of Room Type Per Night',fontsize=25)
plt.xlabel('Room Type',fontsize=15)
plt.ylabel('Average Daily Rate in [EUR]',fontsize=15)

Text(0, 0.5, 'Average Daily Rate in [EUR]')


data_resort=resort[resort['is_canceled']==0]
data_city=city[city['is_canceled']==0]


resort_mean=data_resort.groupby('arrival_date_month')['adr'].mean().reset_index()
resort_mean


city_mean=data_city.groupby('arrival_date_month')['adr'].mean().reset_index()
city_mean


# Merging dataframe using same column
final_mean=resort_mean.merge(city_mean,on='arrival_date_month')

# Renaming columns
final_mean.columns=['Month','Price for Resort Hotel','Price for City Hotel']

final_mean.head()


# Sorting Month
!pip install sorted-months-weekdays

Requirement already satisfied: sorted-months-weekdays in c:\users\yunge\anaconda3\lib\site-packages (0.2)


!pip install sort-dataframeby-monthorweek

Requirement already satisfied: sort-dataframeby-monthorweek in c:\users\yunge\anaconda3\lib\site-packages (0.4)


import sort_dataframeby_monthorweek as sd


final_mean2=sd.Sort_Dataframeby_Month(final_mean,'Month')
final_mean2.head()


px.line(final_mean2,x='Month',y=['Price for Resort Hotel','Price for City Hotel'],title='Room Price Per Night by Month')


data.head()


# Depending on your pandas version and settings, you may get a different number. In our case, the result turned out to be 20. That is, if a dataframe has more than 20 columns, it will skip a few columns when displaying the dataframe.
pd.get_option("display.max_columns")

20


# Settings to display all columns
pd.set_option("display.max_columns", None)


data.head()


data.columns

Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',
       'arrival_date_month', 'arrival_date_week_number',
       'arrival_date_day_of_month', 'stays_in_weekend_nights',
       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',
       'country', 'market_segment', 'distribution_channel',
       'is_repeated_guest', 'previous_cancellations',
       'previous_bookings_not_canceled', 'reserved_room_type',
       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',
       'company', 'days_in_waiting_list', 'customer_type', 'adr',
       'required_car_parking_spaces', 'total_of_special_requests',
       'reservation_status', 'reservation_status_date'],
      dtype='object')


plt.figure(figsize=(15,10))
sns.boxplot(x='market_segment',y='stays_in_weekend_nights',data=data,hue='hotel')

<AxesSubplot:xlabel='market_segment', ylabel='stays_in_weekend_nights'>


plt.figure(figsize=(15,10))
sns.boxplot(x='market_segment',y='stays_in_week_nights',data=data,hue='hotel')

<AxesSubplot:xlabel='market_segment', ylabel='stays_in_week_nights'>


data['meal'].value_counts()

BB           92236
HB           14458
SC           10549
Undefined     1169
FB             798
Name: meal, dtype: int64


data['meal'].value_counts().index

Index(['BB', 'HB', 'SC', 'Undefined', 'FB'], dtype='object')


# 'Hole' to make a donut chart
px.pie(data,values=data['meal'].value_counts(),names=data['meal'].value_counts().index,hole=0.5,title='What is the Most Popular Meal?')


# Majority of the guests have no special requests
sns.countplot(data['total_of_special_requests'])

C:\Users\yunge\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

<AxesSubplot:xlabel='total_of_special_requests', ylabel='count'>


# Majority of the guests have no special requests
data['total_of_special_requests'].value_counts()

0    70201
1    33183
2    12952
3     2494
4      340
5       40
Name: total_of_special_requests, dtype: int64


data['total_of_special_requests'].count()

119210


# Creating a pivot table
pivot_data=data.groupby(['total_of_special_requests','is_canceled']).agg({'total_of_special_requests':'count'})
pivot_data


pivot_data.unstack()


# Rename the column to count
pivot=pivot_data.rename(columns={'total_of_special_requests':'count'}).unstack()
pivot.head()


pivot.plot(kind='bar')

<AxesSubplot:xlabel='total_of_special_requests'>


rush_resort=data_resort['arrival_date_month'].value_counts().reset_index()
rush_resort.columns=['Month','Number of Guests']
rush_resort


rush_city=data_city['arrival_date_month'].value_counts().reset_index()
rush_city.columns=['Month','Number of Guests']
rush_city


# Merging dataframe
final_rush=rush_resort.merge(rush_city,on='Month')
final_rush.columns=['Month','Number of Guest for Resort Hotel','Number of Guest for City Hotel']
final_rush


# Sort month in order
import sort_dataframeby_monthorweek as sd


final_rush2=sd.Sort_Dataframeby_Month(df=final_rush,monthcolumnname='Month')
final_rush2


px.line(data_frame=final_rush2,x='Month',y=['Number of Guest for Resort Hotel','Number of Guest for City Hotel'],title='Total Number of Guests Per Month')


filter2=data['is_canceled']==0
filter2.head()

0    True
1    True
2    True
3    True
4    True
Name: is_canceled, dtype: bool


clean_data=data[filter2]
clean_data.head()


clean_data.shape

(75011, 32)


filter3=data['is_canceled']==1
clean_data2=data[filter3]
clean_data2.head()


clean_data2.shape

(44199, 32)


44199 + 75011

119210


clean_data['total_nights']=clean_data['stays_in_weekend_nights'] + clean_data['stays_in_week_nights']

<ipython-input-81-2d03ba504a81>:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


# Ignore warnings
import warnings
from warnings import filterwarnings
filterwarnings('ignore')


clean_data['total_nights']=clean_data['stays_in_weekend_nights'] + clean_data['stays_in_week_nights']


stay=clean_data.groupby(['total_nights','hotel']).agg('count').reset_index()
stay.head()


# Choose only the first 3 columns
stay=stay.iloc[:,0:3]
stay.head()


# Rename columns
stay=stay.rename(columns={'is_canceled': 'Number of Stays'})
stay.head()


plt.figure(figsize=(20,10))
sns.barplot(x='total_nights',y='Number of Stays',hue='hotel',data=stay)

<AxesSubplot:xlabel='total_nights', ylabel='Number of Stays'>


# Use hue order to prioritize hotels color order
plt.figure(figsize=(20,10))
sns.barplot(x='total_nights',y='Number of Stays',hue='hotel',hue_order=['Resort Hotel','City Hotel'],data=stay)

<AxesSubplot:xlabel='total_nights', ylabel='Number of Stays'>


# Bookings by market segment
clean_data.columns

Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',
       'arrival_date_month', 'arrival_date_week_number',
       'arrival_date_day_of_month', 'stays_in_weekend_nights',
       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',
       'country', 'market_segment', 'distribution_channel',
       'is_repeated_guest', 'previous_cancellations',
       'previous_bookings_not_canceled', 'reserved_room_type',
       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',
       'company', 'days_in_waiting_list', 'customer_type', 'adr',
       'required_car_parking_spaces', 'total_of_special_requests',
       'reservation_status', 'reservation_status_date', 'total_nights'],
      dtype='object')


clean_data['market_segment'].value_counts()

Online TA        35673
Offline TA/TO    15880
Direct           10648
Groups            7697
Corporate         4291
Complementary      639
Aviation           183
Name: market_segment, dtype: int64


clean_data['market_segment'].value_counts().index

Index(['Online TA', 'Offline TA/TO', 'Direct', 'Groups', 'Corporate',
       'Complementary', 'Aviation'],
      dtype='object')


px.pie(clean_data,values=clean_data['market_segment'].value_counts(),names=clean_data['market_segment'].value_counts().index,title='Bookings by Market Segment',hole=0.5)


cancel=data[data['is_canceled']==1]
cancel.head()


# How many resort hotel bookings were cancelled?
len(cancel[cancel['hotel']=='Resort Hotel'])

11120


# How many city hotel bookings were cancelled?
len(cancel[cancel['hotel']=='City Hotel'])

33079


px.pie(values=[11120,33079],names=['Resort Hotel Cancellations','City Hotel Cancellation'],hole=0.5)

	hotel	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_week_nights	adults	...	deposit_type	agent	company	customer_type	adr	total_of_special_requests	reservation_status	reservation_status_date
0	Resort Hotel	342	2015	July	27	1	0	2	...	No Deposit	NaN	NaN	Transient	0.0	0	Check-Out	7/1/2015
1	Resort Hotel	737	2015	July	27	1	0	2	...	No Deposit	NaN	NaN	Transient	0.0	0	Check-Out	7/1/2015
2	Resort Hotel	7	2015	July	27	1	1	1	...	No Deposit	NaN	NaN	Transient	75.0	0	Check-Out	7/2/2015
3	Resort Hotel	13	2015	July	27	1	1	1	...	No Deposit	304.0	NaN	Transient	75.0	0	Check-Out	7/2/2015
4	Resort Hotel	14	2015	July	27	1	2	2	...	No Deposit	240.0	NaN	Transient	98.0	1	Check-Out	7/3/2015

	hotel	is_canceled	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_weekend_nights	stays_in_week_nights	adults	...	deposit_type	agent	company	days_in_waiting_list	customer_type	adr	required_car_parking_spaces	total_of_special_requests	reservation_status	reservation_status_date
2224	Resort Hotel	0	1	2015	October	41	6	0	3	0	...	No Deposit	0.0	174.0	0	Transient-Party	0.00	0	0	Check-Out	10/6/2015
2409	Resort Hotel	0	0	2015	October	42	12	0	0	0	...	No Deposit	0.0	174.0	0	Transient	0.00	0	0	Check-Out	10/12/2015
3181	Resort Hotel	0	36	2015	November	47	20	1	2	0	...	No Deposit	38.0	0.0	0	Transient-Party	0.00	0	0	Check-Out	11/23/2015
3684	Resort Hotel	0	165	2015	December	53	30	1	4	0	...	No Deposit	308.0	0.0	122	Transient-Party	0.00	0	0	Check-Out	1/4/2016
3708	Resort Hotel	0	165	2015	December	53	30	2	4	0	...	No Deposit	308.0	0.0	122	Transient-Party	0.00	0	0	Check-Out	1/5/2016
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
115029	City Hotel	0	107	2017	June	26	27	0	3	0	...	No Deposit	7.0	0.0	0	Transient	100.80	0	0	Check-Out	6/30/2017
115091	City Hotel	0	1	2017	June	26	30	0	1	0	...	No Deposit	0.0	0.0	0	Transient	0.00	1	1	Check-Out	7/1/2017
116251	City Hotel	0	44	2017	July	28	15	1	1	0	...	No Deposit	425.0	0.0	0	Transient	73.80	0	0	Check-Out	7/17/2017
116534	City Hotel	0	2	2017	July	28	15	2	5	0	...	No Deposit	9.0	0.0	0	Transient-Party	22.86	0	1	Check-Out	7/22/2017
117087	City Hotel	0	170	2017	July	30	27	0	2	0	...	No Deposit	52.0	0.0	0	Transient	0.00	0	0	Check-Out	7/29/2017

	hotel	is_canceled	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_weekend_nights	stays_in_week_nights	adults	...	deposit_type	agent	company	days_in_waiting_list	customer_type	adr	required_car_parking_spaces	total_of_special_requests	reservation_status	reservation_status_date
0	Resort Hotel	0	342	2015	July	27	1	0	0	2	...	No Deposit	0.0	0.0	0	Transient	0.00	0	0	Check-Out	7/1/2015
1	Resort Hotel	0	737	2015	July	27	1	0	0	2	...	No Deposit	0.0	0.0	0	Transient	0.00	0	0	Check-Out	7/1/2015
2	Resort Hotel	0	7	2015	July	27	1	0	1	1	...	No Deposit	0.0	0.0	0	Transient	75.00	0	0	Check-Out	7/2/2015
3	Resort Hotel	0	13	2015	July	27	1	0	1	1	...	No Deposit	304.0	0.0	0	Transient	75.00	0	0	Check-Out	7/2/2015
4	Resort Hotel	0	14	2015	July	27	1	0	2	2	...	No Deposit	240.0	0.0	0	Transient	98.00	0	1	Check-Out	7/3/2015
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
119385	City Hotel	0	23	2017	August	35	30	2	5	2	...	No Deposit	394.0	0.0	0	Transient	96.14	0	0	Check-Out	9/6/2017
119386	City Hotel	0	102	2017	August	35	31	2	5	3	...	No Deposit	9.0	0.0	0	Transient	225.43	0	2	Check-Out	9/7/2017
119387	City Hotel	0	34	2017	August	35	31	2	5	2	...	No Deposit	9.0	0.0	0	Transient	157.71	0	4	Check-Out	9/7/2017
119388	City Hotel	0	109	2017	August	35	31	2	5	2	...	No Deposit	89.0	0.0	0	Transient	104.40	0	0	Check-Out	9/7/2017
119389	City Hotel	0	205	2017	August	35	29	2	7	2	...	No Deposit	9.0	0.0	0	Transient	151.20	0	2	Check-Out	9/7/2017

	hotel	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_week_nights	adults	...	deposit_type	agent	customer_type	adr	total_of_special_requests	reservation_status	reservation_status_date
0	Resort Hotel	342	2015	July	27	1	0	2	...	No Deposit	0.0	Transient	0.0	0	Check-Out	7/1/2015
1	Resort Hotel	737	2015	July	27	1	0	2	...	No Deposit	0.0	Transient	0.0	0	Check-Out	7/1/2015
2	Resort Hotel	7	2015	July	27	1	1	1	...	No Deposit	0.0	Transient	75.0	0	Check-Out	7/2/2015
3	Resort Hotel	13	2015	July	27	1	1	1	...	No Deposit	304.0	Transient	75.0	0	Check-Out	7/2/2015
4	Resort Hotel	14	2015	July	27	1	2	2	...	No Deposit	240.0	Transient	98.0	1	Check-Out	7/3/2015

	hotel	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_week_nights	adults	...	deposit_type	agent	customer_type	adr	reservation_status	reservation_status_date
40060	City Hotel	6	2015	July	27	1	2	1	...	No Deposit	6.0	Transient	0.00	Check-Out	7/3/2015
40066	City Hotel	3	2015	July	27	2	3	1	...	No Deposit	1.0	Transient-Party	58.67	Check-Out	7/5/2015
40070	City Hotel	43	2015	July	27	3	2	2	...	No Deposit	1.0	Transient-Party	86.00	Check-Out	7/5/2015
40071	City Hotel	43	2015	July	27	3	2	2	...	No Deposit	1.0	Transient-Party	43.00	Check-Out	7/5/2015
40072	City Hotel	43	2015	July	27	3	2	2	...	No Deposit	1.0	Transient-Party	86.00	Check-Out	7/5/2015

Hotel Booking Analysis¶

Import Libraries¶

Loading Dataset¶

Data Preperation¶

Spatial Analysis on Guests' Hometown¶

Import Plotly¶

Hotel Pricing Analysis¶

Preference of Guests¶

Analyzing Relationship Between Special Guests and Cancellation¶

Most Busy Months¶

Analyzing More About Customers¶

How Many Bookings were Cancelled?¶

	arrival_date_month	adr
0	April	75.867816
1	August	181.205892
2	December	68.410104
3	February	54.147478
4	January	48.761125
5	July	150.122528
6	June	107.974850
7	March	57.056838
8	May	76.657558
9	November	48.706289
10	October	61.775449
11	September	96.416860

	arrival_date_month	adr
0	April	111.962267
1	August	118.674598
2	December	88.401855
3	February	86.520062
4	January	82.330983
5	July	115.818019
6	June	117.874360
7	March	90.658533
8	May	120.669827
9	November	86.946592
10	October	102.004672
11	September	112.776582

		total_of_special_requests
total_of_special_requests	is_canceled
0	0	36667
0	1	33534
1	0	25867
1	1	7316
2	0	10086
2	1	2866
3	0	2049
3	1	445
4	0	304
4	1	36
5	0	38
5	1	2

	Month	Number of Guests
0	August	3257
1	July	3137
2	October	2575
3	March	2571
4	April	2550
5	May	2535
6	February	2308
7	September	2102
8	June	2037
9	December	2014
10	November	1975
11	January	1866

	Month	Number of Guests
0	August	5367
1	July	4770
2	May	4568
3	June	4358
4	October	4326
5	September	4283
6	March	4049
7	April	4010
8	February	3051
9	November	2676
10	December	2377
11	January	2249

	total_nights	hotel	is_canceled	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_weekend_nights	stays_in_week_nights	adults	children	babies	meal	country	market_segment	distribution_channel	is_repeated_guest	previous_cancellations	previous_bookings_not_canceled	reserved_room_type	assigned_room_type	booking_changes	deposit_type	agent	company	days_in_waiting_list	customer_type	adr	required_car_parking_spaces	total_of_special_requests	reservation_status	reservation_status_date
0	0	City Hotel	251	251	251	251	251	251	251	251	251	251	251	251	251	251	251	251	251	251	251	251	251	251	251	251	251	251	251	251	251	251	251
1	0	Resort Hotel	371	371	371	371	371	371	371	371	371	371	371	371	371	371	371	371	371	371	371	371	371	371	371	371	371	371	371	371	371	371	371
2	1	City Hotel	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155	9155
3	1	Resort Hotel	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579	6579
4	2	City Hotel	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983	10983