📚 Importing Libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
sns.set_style("darkgrid")

📥 Load Dataset

df=pd.read_csv('C:/Users/Ambuj nayan mishra/Downloads/hotel_booking.csv')

df.head()

	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	...	customer_type	adr	required_car_parking_spaces	total_of_special_requests	reservation_status	reservation_status_date	name	email	phone-number	credit_card
0	Resort Hotel	0	342	2015	July	27	1	0	0	2	...	Transient	0.0	0	0	Check-Out	2015-07-01	Ernest Barnes	Ernest.Barnes31@outlook.com	669-792-1661	************4322
1	Resort Hotel	0	737	2015	July	27	1	0	0	2	...	Transient	0.0	0	0	Check-Out	2015-07-01	Andrea Baker	Andrea_Baker94@aol.com	858-637-6955	************9157
2	Resort Hotel	0	7	2015	July	27	1	0	1	1	...	Transient	75.0	0	0	Check-Out	2015-07-02	Rebecca Parker	Rebecca_Parker@comcast.net	652-885-2745	************3734
3	Resort Hotel	0	13	2015	July	27	1	0	1	1	...	Transient	75.0	0	0	Check-Out	2015-07-02	Laura Murray	Laura_M@gmail.com	364-656-8427	************5677
4	Resort Hotel	0	14	2015	July	27	1	0	2	2	...	Transient	98.0	0	1	Check-Out	2015-07-03	Linda Hines	LHines@verizon.com	713-226-5883	************5498

5 rows × 36 columns

df.tail()

	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	...	customer_type	adr	required_car_parking_spaces	total_of_special_requests	reservation_status	reservation_status_date	name	email	phone-number	credit_card
119385	City Hotel	0	23	2017	August	35	30	2	5	2	...	Transient	96.14	0	0	Check-Out	2017-09-06	Claudia Johnson	Claudia.J@yahoo.com	403-092-5582	************8647
119386	City Hotel	0	102	2017	August	35	31	2	5	3	...	Transient	225.43	0	2	Check-Out	2017-09-07	Wesley Aguilar	WAguilar@xfinity.com	238-763-0612	************4333
119387	City Hotel	0	34	2017	August	35	31	2	5	2	...	Transient	157.71	0	4	Check-Out	2017-09-07	Mary Morales	Mary_Morales@hotmail.com	395-518-4100	************1821
119388	City Hotel	0	109	2017	August	35	31	2	5	2	...	Transient	104.40	0	0	Check-Out	2017-09-07	Caroline Conley MD	MD_Caroline@comcast.net	531-528-1017	************7860
119389	City Hotel	0	205	2017	August	35	29	2	7	2	...	Transient	151.20	0	2	Check-Out	2017-09-07	Ariana Michael	Ariana_M@xfinity.com	422-804-6403	************4482

5 rows × 36 columns

📝 About Dataset:

Context

This dataset contains 119390 observations for a City Hotel and a Resort Hotel. Each observation represents a hotel booking between the 1st of July 2015 and 31st of August 2017, including booking that effectively arrived and booking that were canceled.

Content

Since this is hotel real data, all data elements pertaining hotel or costumer identification were deleted. Four Columns, 'name', 'email', 'phone number' and 'credit_card' have been artificially created and added to the dataset.

Acknowledgements

The data is originally from the article Hotel Booking Demand Datasets, written by Nuno Antonio, Ana Almeida, and Luis Nunes for Data in Brief, Volume 22, February 2019.

No	Columns name	Meaning
1	hotel	The datasets contains the booking information of two hotel. One of the hotels is a resort hotel and the other is a city
2	is_canceled	Value indicating if the booking was canceled (1) or not (0).
3	country	Country of origin.
4	market_segment	Market segment designation. In categories, the term “TA” means “Travel Agents” and “TO” means “Tour Operators”.
5	adr	Average Daily Rate (Calculated by dividing the sum of all lodging transactions by the total number of staying nights).

Basic info about dataset

r,c=df.shape
print(f'number of rows:{r}')
print(f'number of columns:{c}')

number of rows:119390
number of columns:36

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 119390 entries, 0 to 119389
Data columns (total 36 columns):
 #   Column                          Non-Null Count   Dtype  
---  ------                          --------------   -----  
 0   hotel                           119390 non-null  object 
 1   is_canceled                     119390 non-null  int64  
 2   lead_time                       119390 non-null  int64  
 3   arrival_date_year               119390 non-null  int64  
 4   arrival_date_month              119390 non-null  object 
 5   arrival_date_week_number        119390 non-null  int64  
 6   arrival_date_day_of_month       119390 non-null  int64  
 7   stays_in_weekend_nights         119390 non-null  int64  
 8   stays_in_week_nights            119390 non-null  int64  
 9   adults                          119390 non-null  int64  
 10  children                        119386 non-null  float64
 11  babies                          119390 non-null  int64  
 12  meal                            119390 non-null  object 
 13  country                         118902 non-null  object 
 14  market_segment                  119390 non-null  object 
 15  distribution_channel            119390 non-null  object 
 16  is_repeated_guest               119390 non-null  int64  
 17  previous_cancellations          119390 non-null  int64  
 18  previous_bookings_not_canceled  119390 non-null  int64  
 19  reserved_room_type              119390 non-null  object 
 20  assigned_room_type              119390 non-null  object 
 21  booking_changes                 119390 non-null  int64  
 22  deposit_type                    119390 non-null  object 
 23  agent                           103050 non-null  float64
 24  company                         6797 non-null    float64
 25  days_in_waiting_list            119390 non-null  int64  
 26  customer_type                   119390 non-null  object 
 27  adr                             119390 non-null  float64
 28  required_car_parking_spaces     119390 non-null  int64  
 29  total_of_special_requests       119390 non-null  int64  
 30  reservation_status              119390 non-null  object 
 31  reservation_status_date         119390 non-null  object 
 32  name                            119390 non-null  object 
 33  email                           119390 non-null  object 
 34  phone-number                    119390 non-null  object 
 35  credit_card                     119390 non-null  object 
dtypes: float64(4), int64(16), object(16)
memory usage: 32.8+ MB

df.describe()

	is_canceled	lead_time	arrival_date_year	arrival_date_week_number	arrival_date_day_of_month	stays_in_weekend_nights	stays_in_week_nights	adults	children	babies	is_repeated_guest	previous_cancellations	previous_bookings_not_canceled	booking_changes	agent	company	days_in_waiting_list	adr	required_car_parking_spaces	total_of_special_requests
count	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000	119386.000000	119390.000000	119390.000000	119390.000000	119390.000000	119390.000000	103050.000000	6797.000000	119390.000000	119390.000000	119390.000000	119390.000000
mean	0.370416	104.011416	2016.156554	27.165173	15.798241	0.927599	2.500302	1.856403	0.103890	0.007949	0.031912	0.087118	0.137097	0.221124	86.693382	189.266735	2.321149	101.831122	0.062518	0.571363
std	0.482918	106.863097	0.707476	13.605138	8.780829	0.998613	1.908286	0.579261	0.398561	0.097436	0.175767	0.844336	1.497437	0.652306	110.774548	131.655015	17.594721	50.535790	0.245291	0.792798
min	0.000000	0.000000	2015.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	6.000000	0.000000	-6.380000	0.000000	0.000000
25%	0.000000	18.000000	2016.000000	16.000000	8.000000	0.000000	1.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	9.000000	62.000000	0.000000	69.290000	0.000000	0.000000
50%	0.000000	69.000000	2016.000000	28.000000	16.000000	1.000000	2.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	14.000000	179.000000	0.000000	94.575000	0.000000	0.000000
75%	1.000000	160.000000	2017.000000	38.000000	23.000000	2.000000	3.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	229.000000	270.000000	0.000000	126.000000	0.000000	1.000000
max	1.000000	737.000000	2017.000000	53.000000	31.000000	19.000000	50.000000	55.000000	10.000000	10.000000	1.000000	26.000000	72.000000	21.000000	535.000000	543.000000	391.000000	5400.000000	8.000000	5.000000

df.describe(include='object')

	hotel	arrival_date_month	meal	country	market_segment	distribution_channel	reserved_room_type	assigned_room_type	deposit_type	customer_type	reservation_status	reservation_status_date	name	email	phone-number	credit_card
count	119390	119390	119390	118902	119390	119390	119390	119390	119390	119390	119390	119390	119390	119390	119390	119390
unique	2	12	5	177	8	5	10	12	3	4	3	926	81503	115889	119390	9000
top	City Hotel	August	BB	PRT	Online TA	TA/TO	A	A	No Deposit	Transient	Check-Out	2015-10-21	Michael Johnson	Michael.C@gmail.com	669-792-1661	************4923
freq	79330	13877	92310	48590	56477	97870	85994	74053	104641	89613	75166	1461	48	6	1	28

for i in df.describe(include='object').columns:
    print(i)
    print(df[i].unique())
    print('-------------------------------------------------')

hotel
['Resort Hotel' 'City Hotel']
-------------------------------------------------
arrival_date_month
['July' 'August' 'September' 'October' 'November' 'December' 'January'
 'February' 'March' 'April' 'May' 'June']
-------------------------------------------------
meal
['BB' 'FB' 'HB' 'SC' 'Undefined']
-------------------------------------------------
country
['PRT' 'GBR' 'USA' 'ESP' 'IRL' 'FRA' nan 'ROU' 'NOR' 'OMN' 'ARG' 'POL'
 'DEU' 'BEL' 'CHE' 'CN' 'GRC' 'ITA' 'NLD' 'DNK' 'RUS' 'SWE' 'AUS' 'EST'
 'CZE' 'BRA' 'FIN' 'MOZ' 'BWA' 'LUX' 'SVN' 'ALB' 'IND' 'CHN' 'MEX' 'MAR'
 'UKR' 'SMR' 'LVA' 'PRI' 'SRB' 'CHL' 'AUT' 'BLR' 'LTU' 'TUR' 'ZAF' 'AGO'
 'ISR' 'CYM' 'ZMB' 'CPV' 'ZWE' 'DZA' 'KOR' 'CRI' 'HUN' 'ARE' 'TUN' 'JAM'
 'HRV' 'HKG' 'IRN' 'GEO' 'AND' 'GIB' 'URY' 'JEY' 'CAF' 'CYP' 'COL' 'GGY'
 'KWT' 'NGA' 'MDV' 'VEN' 'SVK' 'FJI' 'KAZ' 'PAK' 'IDN' 'LBN' 'PHL' 'SEN'
 'SYC' 'AZE' 'BHR' 'NZL' 'THA' 'DOM' 'MKD' 'MYS' 'ARM' 'JPN' 'LKA' 'CUB'
 'CMR' 'BIH' 'MUS' 'COM' 'SUR' 'UGA' 'BGR' 'CIV' 'JOR' 'SYR' 'SGP' 'BDI'
 'SAU' 'VNM' 'PLW' 'QAT' 'EGY' 'PER' 'MLT' 'MWI' 'ECU' 'MDG' 'ISL' 'UZB'
 'NPL' 'BHS' 'MAC' 'TGO' 'TWN' 'DJI' 'STP' 'KNA' 'ETH' 'IRQ' 'HND' 'RWA'
 'KHM' 'MCO' 'BGD' 'IMN' 'TJK' 'NIC' 'BEN' 'VGB' 'TZA' 'GAB' 'GHA' 'TMP'
 'GLP' 'KEN' 'LIE' 'GNB' 'MNE' 'UMI' 'MYT' 'FRO' 'MMR' 'PAN' 'BFA' 'LBY'
 'MLI' 'NAM' 'BOL' 'PRY' 'BRB' 'ABW' 'AIA' 'SLV' 'DMA' 'PYF' 'GUY' 'LCA'
 'ATA' 'GTM' 'ASM' 'MRT' 'NCL' 'KIR' 'SDN' 'ATF' 'SLE' 'LAO']
-------------------------------------------------
market_segment
['Direct' 'Corporate' 'Online TA' 'Offline TA/TO' 'Complementary' 'Groups'
 'Undefined' 'Aviation']
-------------------------------------------------
distribution_channel
['Direct' 'Corporate' 'TA/TO' 'Undefined' 'GDS']
-------------------------------------------------
reserved_room_type
['C' 'A' 'D' 'E' 'G' 'F' 'H' 'L' 'P' 'B']
-------------------------------------------------
assigned_room_type
['C' 'A' 'D' 'E' 'G' 'F' 'I' 'B' 'H' 'P' 'L' 'K']
-------------------------------------------------
deposit_type
['No Deposit' 'Refundable' 'Non Refund']
-------------------------------------------------
customer_type
['Transient' 'Contract' 'Transient-Party' 'Group']
-------------------------------------------------
reservation_status
['Check-Out' 'Canceled' 'No-Show']
-------------------------------------------------
reservation_status_date
['2015-07-01' '2015-07-02' '2015-07-03' '2015-05-06' '2015-04-22'
 '2015-06-23' '2015-07-05' '2015-07-06' '2015-07-07' '2015-07-08'
 '2015-05-11' '2015-07-15' '2015-07-16' '2015-05-29' '2015-05-19'
 '2015-06-19' '2015-05-23' '2015-05-18' '2015-07-09' '2015-06-02'
 '2015-07-13' '2015-07-04' '2015-06-29' '2015-06-16' '2015-06-18'
 '2015-06-12' '2015-06-09' '2015-05-26' '2015-07-11' '2015-07-12'
 '2015-07-17' '2015-04-15' '2015-05-13' '2015-07-10' '2015-05-20'
 '2015-05-12' '2015-07-14' '2015-06-17' '2015-05-01' '2015-03-30'
 '2015-07-19' '2015-06-03' '2015-06-26' '2015-05-14' '2015-07-20'
 '2015-05-07' '2015-05-28' '2015-04-13' '2015-03-25' '2015-07-21'
 '2015-06-27' '2015-07-18' '2015-07-23' '2015-06-08' '2015-06-22'
 '2015-06-24' '2015-03-05' '2015-06-01' '2015-04-24' '2015-07-22'
 '2015-05-27' '2015-04-06' '2015-04-11' '2015-07-25' '2015-07-28'
 '2015-07-29' '2015-06-25' '2015-07-24' '2015-06-05' '2015-06-30'
 '2015-06-13' '2015-06-11' '2015-07-30' '2015-07-27' '2015-04-29'
 '2015-06-04' '2015-07-26' '2015-08-01' '2015-08-02' '2015-06-15'
 '2015-04-23' '2015-07-31' '2015-05-25' '2015-08-03' '2015-04-17'
 '2015-08-04' '2015-08-06' '2015-05-15' '2015-05-09' '2015-03-17'
 '2015-05-22' '2015-08-07' '2015-04-04' '2015-08-05' '2015-08-08'
 '2015-08-10' '2015-05-04' '2015-06-06' '2015-08-09' '2015-08-15'
 '2015-08-11' '2015-03-28' '2015-08-14' '2015-08-12' '2015-08-16'
 '2015-05-16' '2015-08-21' '2015-08-13' '2015-08-17' '2015-04-20'
 '2015-08-18' '2015-08-23' '2015-08-22' '2015-08-19' '2015-08-20'
 '2015-08-29' '2015-03-31' '2015-05-30' '2015-08-25' '2015-04-14'
 '2015-08-24' '2015-03-24' '2015-05-21' '2015-08-28' '2015-08-26'
 '2015-08-27' '2015-08-30' '2015-08-31' '2015-09-06' '2015-09-03'
 '2015-09-04' '2015-09-02' '2015-09-01' '2015-09-05' '2015-06-20'
 '2015-09-07' '2015-09-10' '2015-09-11' '2015-09-08' '2015-09-09'
 '2015-09-13' '2015-09-15' '2015-04-10' '2015-01-02' '2014-11-18'
 '2015-09-12' '2015-09-17' '2015-09-14' '2015-04-07' '2015-09-19'
 '2015-09-16' '2015-09-20' '2015-01-18' '2015-10-23' '2015-01-22'
 '2015-01-01' '2015-09-22' '2015-09-24' '2015-09-18' '2015-09-21'
 '2015-09-30' '2015-09-25' '2015-09-27' '2015-09-28' '2015-10-12'
 '2015-09-29' '2015-09-23' '2015-10-01' '2015-09-26' '2015-04-18'
 '2015-10-02' '2015-10-04' '2015-10-08' '2015-10-03' '2015-10-07'
 '2015-10-09' '2015-10-11' '2015-10-05' '2015-10-06' '2015-10-10'
 '2015-10-14' '2015-10-15' '2015-10-18' '2015-10-13' '2015-10-20'
 '2015-10-19' '2015-10-31' '2015-10-16' '2015-10-21' '2015-10-22'
 '2015-10-17' '2015-10-24' '2015-10-25' '2015-10-28' '2015-10-27'
 '2015-10-26' '2015-10-30' '2015-11-05' '2015-10-29' '2015-11-03'
 '2015-11-07' '2015-11-04' '2015-11-01' '2015-11-02' '2015-11-17'
 '2015-11-06' '2015-11-10' '2015-11-08' '2015-11-09' '2015-11-15'
 '2015-11-16' '2015-11-11' '2015-11-12' '2015-11-14' '2015-11-13'
 '2015-11-18' '2015-11-22' '2015-11-19' '2015-11-21' '2015-11-20'
 '2015-11-24' '2015-11-25' '2015-11-23' '2015-11-28' '2015-11-26'
 '2015-11-27' '2015-11-29' '2015-12-04' '2015-12-01' '2015-12-06'
 '2015-12-08' '2015-12-02' '2015-12-03' '2015-12-31' '2015-12-05'
 '2015-12-10' '2015-12-17' '2015-11-30' '2015-12-12' '2015-12-07'
 '2016-01-05' '2015-12-11' '2015-12-13' '2015-12-15' '2015-12-16'
 '2015-12-19' '2015-12-18' '2015-12-26' '2015-12-27' '2015-12-22'
 '2015-12-23' '2015-12-24' '2015-12-29' '2015-12-28' '2015-12-20'
 '2015-12-30' '2016-01-02' '2016-01-01' '2015-12-25' '2016-01-03'
 '2016-01-04' '2016-01-11' '2016-01-07' '2015-12-21' '2016-01-09'
 '2016-01-10' '2016-01-08' '2016-01-06' '2016-01-12' '2016-01-13'
 '2016-01-23' '2016-02-09' '2016-01-15' '2016-01-16' '2016-01-17'
 '2016-01-19' '2016-01-18' '2016-01-21' '2016-01-24' '2016-01-22'
 '2016-01-29' '2016-01-27' '2016-01-25' '2016-03-08' '2016-01-26'
 '2016-01-20' '2016-01-30' '2016-02-01' '2016-02-02' '2016-02-08'
 '2016-02-07' '2016-01-28' '2016-02-05' '2016-02-03' '2016-02-13'
 '2016-02-10' '2016-02-04' '2016-02-12' '2016-02-11' '2016-02-16'
 '2016-02-14' '2016-02-15' '2016-02-20' '2016-02-06' '2016-01-14'
 '2016-02-17' '2016-02-21' '2016-02-24' '2016-02-25' '2016-02-19'
 '2016-02-18' '2016-02-26' '2016-02-23' '2016-03-05' '2016-02-22'
 '2016-02-27' '2016-03-03' '2016-03-24' '2016-03-04' '2016-02-29'
 '2016-03-01' '2016-03-02' '2016-03-30' '2016-03-07' '2016-03-14'
 '2016-03-21' '2016-03-09' '2016-03-12' '2016-03-22' '2016-03-10'
 '2016-03-11' '2016-03-20' '2016-03-15' '2016-03-17' '2016-03-16'
 '2016-03-19' '2016-03-27' '2016-03-18' '2016-03-26' '2016-03-31'
 '2016-03-28' '2016-03-29' '2016-04-01' '2016-03-23' '2016-04-02'
 '2016-03-25' '2016-03-13' '2016-04-04' '2016-04-03' '2016-04-05'
 '2016-04-08' '2016-04-06' '2016-04-09' '2016-04-12' '2016-04-16'
 '2016-04-17' '2016-04-27' '2016-04-14' '2016-04-18' '2016-04-21'
 '2016-04-19' '2016-04-20' '2016-04-10' '2016-04-13' '2016-04-11'
 '2016-04-07' '2016-04-15' '2016-04-22' '2016-04-23' '2016-04-26'
 '2016-04-28' '2016-04-24' '2016-04-25' '2016-04-29' '2016-04-30'
 '2016-05-01' '2016-05-10' '2016-05-02' '2016-05-07' '2016-05-08'
 '2016-05-12' '2016-05-04' '2016-05-06' '2016-05-03' '2016-05-09'
 '2016-05-05' '2016-05-13' '2016-05-14' '2016-05-18' '2016-05-19'
 '2016-05-15' '2016-05-16' '2016-05-11' '2016-05-21' '2016-05-22'
 '2016-05-20' '2016-05-24' '2016-05-25' '2016-05-26' '2016-05-23'
 '2016-05-27' '2016-05-17' '2016-05-29' '2016-05-28' '2016-05-30'
 '2016-05-31' '2016-06-01' '2016-06-03' '2016-06-08' '2016-06-02'
 '2016-06-05' '2016-06-06' '2016-06-13' '2016-06-07' '2016-06-10'
 '2016-06-11' '2016-06-16' '2016-06-12' '2016-06-14' '2016-06-17'
 '2016-06-04' '2016-06-18' '2016-06-21' '2016-06-09' '2016-06-24'
 '2016-06-20' '2016-06-25' '2016-06-22' '2016-06-26' '2016-06-23'
 '2016-07-01' '2016-06-15' '2016-06-28' '2016-07-02' '2016-06-19'
 '2016-06-27' '2016-07-04' '2016-06-30' '2016-07-05' '2016-07-08'
 '2016-07-09' '2016-07-07' '2016-07-12' '2016-06-29' '2016-07-10'
 '2016-07-15' '2016-07-03' '2016-07-16' '2016-07-14' '2016-07-18'
 '2016-07-13' '2016-07-06' '2016-07-20' '2016-07-21' '2016-07-23'
 '2016-07-19' '2016-07-11' '2016-07-28' '2016-07-17' '2016-07-25'
 '2016-07-22' '2016-07-29' '2016-08-03' '2016-08-02' '2016-08-04'
 '2016-08-08' '2016-08-10' '2016-08-01' '2016-08-06' '2016-03-06'
 '2016-08-05' '2016-07-26' '2016-08-07' '2016-07-30' '2016-07-24'
 '2016-08-12' '2016-07-27' '2016-08-13' '2016-08-18' '2016-08-16'
 '2016-08-15' '2016-08-17' '2016-08-11' '2016-07-31' '2016-08-19'
 '2016-09-01' '2016-08-23' '2016-08-26' '2016-08-20' '2016-08-21'
 '2016-09-04' '2016-08-22' '2016-08-27' '2016-08-25' '2016-08-09'
 '2016-09-05' '2016-08-24' '2016-09-10' '2016-08-29' '2016-09-09'
 '2016-08-30' '2016-09-13' '2016-08-31' '2016-09-14' '2016-09-12'
 '2016-09-15' '2016-08-14' '2016-09-02' '2016-09-08' '2016-09-19'
 '2016-09-16' '2016-09-07' '2016-09-21' '2016-09-06' '2016-09-22'
 '2016-09-17' '2016-09-20' '2016-09-03' '2016-09-26' '2016-09-23'
 '2016-09-18' '2016-09-29' '2016-10-02' '2016-10-01' '2016-09-27'
 '2016-09-25' '2016-10-05' '2016-09-11' '2016-09-30' '2016-10-09'
 '2016-10-03' '2016-10-06' '2016-10-11' '2016-09-24' '2016-10-13'
 '2016-09-28' '2016-10-08' '2016-10-07' '2016-10-16' '2016-08-28'
 '2016-10-17' '2016-10-18' '2016-10-10' '2016-10-04' '2016-10-15'
 '2016-10-19' '2016-10-21' '2016-10-12' '2016-10-24' '2016-10-26'
 '2016-10-23' '2016-10-20' '2016-10-25' '2016-10-27' '2016-10-28'
 '2016-10-30' '2016-10-29' '2016-11-01' '2016-11-04' '2016-10-14'
 '2016-11-07' '2016-11-03' '2016-11-10' '2016-11-14' '2016-11-02'
 '2016-10-31' '2016-11-11' '2016-11-08' '2016-11-05' '2016-11-25'
 '2016-11-09' '2016-11-20' '2016-11-21' '2016-10-22' '2016-11-22'
 '2016-11-16' '2016-11-23' '2016-11-17' '2016-11-06' '2016-11-15'
 '2016-11-13' '2016-11-12' '2016-11-27' '2016-11-19' '2016-11-30'
 '2016-11-18' '2016-12-02' '2016-12-04' '2016-11-29' '2016-12-07'
 '2016-11-28' '2016-12-03' '2016-12-06' '2016-11-24' '2016-12-08'
 '2016-12-05' '2016-12-10' '2016-12-13' '2016-12-14' '2016-12-16'
 '2016-12-15' '2016-12-17' '2016-12-19' '2016-12-21' '2016-12-20'
 '2016-12-22' '2016-12-23' '2016-12-24' '2016-12-01' '2016-12-27'
 '2016-12-29' '2016-12-30' '2016-12-12' '2017-01-02' '2016-12-11'
 '2017-01-03' '2017-01-04' '2017-01-01' '2016-12-26' '2017-01-06'
 '2016-12-28' '2016-12-18' '2017-01-10' '2017-01-11' '2017-01-07'
 '2017-01-12' '2017-01-16' '2017-01-14' '2017-01-13' '2017-01-05'
 '2017-01-17' '2017-01-20' '2016-12-09' '2017-01-26' '2016-12-31'
 '2017-01-23' '2017-01-27' '2017-01-28' '2017-01-19' '2017-01-25'
 '2017-01-24' '2017-01-29' '2017-01-18' '2016-12-25' '2017-01-15'
 '2017-01-21' '2017-02-01' '2017-02-02' '2017-01-31' '2017-02-03'
 '2017-02-04' '2017-02-06' '2017-02-07' '2017-02-08' '2017-01-30'
 '2017-02-09' '2017-01-09' '2017-02-11' '2017-02-10' '2017-02-12'
 '2017-02-13' '2017-02-14' '2017-02-16' '2017-02-17' '2017-02-18'
 '2017-02-19' '2017-02-20' '2017-02-15' '2017-02-21' '2017-02-22'
 '2017-02-26' '2017-02-23' '2017-02-24' '2017-02-25' '2017-02-28'
 '2017-03-05' '2017-02-27' '2017-03-03' '2017-03-06' '2017-03-02'
 '2017-03-08' '2017-03-09' '2017-03-10' '2017-03-07' '2017-03-12'
 '2017-03-13' '2017-03-14' '2017-03-01' '2017-03-18' '2017-03-17'
 '2017-03-24' '2017-03-22' '2017-03-26' '2017-03-27' '2017-03-11'
 '2017-03-28' '2017-03-29' '2017-03-30' '2017-03-31' '2017-03-19'
 '2017-01-22' '2017-04-02' '2017-03-20' '2017-04-03' '2017-01-08'
 '2017-03-23' '2017-04-05' '2017-02-05' '2017-04-04' '2017-03-15'
 '2017-04-07' '2017-03-25' '2017-04-08' '2017-04-06' '2017-03-21'
 '2017-04-10' '2017-04-01' '2017-04-11' '2017-04-13' '2017-04-15'
 '2017-04-12' '2017-03-04' '2017-04-19' '2017-04-22' '2017-04-20'
 '2017-05-02' '2017-04-09' '2017-04-23' '2017-04-24' '2017-04-16'
 '2017-04-28' '2017-04-18' '2017-04-26' '2017-04-25' '2017-04-17'
 '2017-04-21' '2017-05-03' '2017-05-04' '2017-03-16' '2017-05-05'
 '2017-04-29' '2017-04-14' '2017-05-08' '2017-04-27' '2017-05-11'
 '2017-05-01' '2017-05-10' '2017-05-13' '2017-05-06' '2017-05-14'
 '2017-05-16' '2017-04-30' '2017-05-15' '2017-05-07' '2017-05-09'
 '2017-05-17' '2017-05-21' '2017-05-12' '2017-05-22' '2017-05-24'
 '2017-05-23' '2017-05-25' '2017-05-26' '2017-05-28' '2017-05-27'
 '2017-05-29' '2017-05-19' '2017-05-31' '2017-05-20' '2017-06-01'
 '2017-05-30' '2017-06-02' '2016-11-26' '2017-06-04' '2017-06-05'
 '2017-06-06' '2017-06-07' '2017-05-18' '2017-06-09' '2017-06-10'
 '2017-06-11' '2017-06-12' '2017-06-14' '2017-06-08' '2017-06-16'
 '2017-06-13' '2017-06-03' '2017-06-24' '2017-06-20' '2017-06-19'
 '2017-06-21' '2017-06-26' '2017-06-27' '2017-06-22' '2017-06-28'
 '2017-06-15' '2017-06-29' '2017-06-30' '2017-06-18' '2017-07-04'
 '2017-07-08' '2017-07-05' '2017-07-03' '2017-07-07' '2017-07-01'
 '2017-07-06' '2017-07-11' '2017-07-12' '2017-06-23' '2017-07-13'
 '2017-07-02' '2017-07-10' '2017-07-14' '2017-07-15' '2017-07-16'
 '2017-07-18' '2017-07-17' '2017-07-19' '2017-07-20' '2017-07-21'
 '2017-06-25' '2017-06-17' '2017-07-24' '2017-07-26' '2017-07-09'
 '2017-07-27' '2017-07-28' '2017-07-31' '2017-07-29' '2017-07-22'
 '2017-08-02' '2017-08-01' '2017-08-03' '2017-08-04' '2017-07-25'
 '2017-07-23' '2017-08-09' '2017-08-10' '2017-07-30' '2017-08-07'
 '2017-08-13' '2017-08-05' '2017-08-14' '2017-08-08' '2017-08-16'
 '2017-08-17' '2017-08-15' '2017-08-18' '2017-08-20' '2017-08-22'
 '2017-08-06' '2017-08-25' '2017-08-26' '2017-08-23' '2017-08-11'
 '2017-08-27' '2017-08-21' '2017-08-29' '2017-08-31' '2017-08-12'
 '2017-08-19' '2016-01-31' '2017-09-01' '2017-08-28' '2015-04-03'
 '2015-01-21' '2015-01-28' '2015-01-29' '2015-01-30' '2015-02-02'
 '2015-02-05' '2015-02-06' '2015-02-09' '2015-02-10' '2015-02-11'
 '2015-02-12' '2015-02-19' '2015-02-20' '2015-02-23' '2015-02-24'
 '2015-02-25' '2015-02-26' '2015-02-27' '2015-03-03' '2015-03-04'
 '2015-03-06' '2015-03-09' '2015-03-11' '2015-03-12' '2015-03-18'
 '2015-04-02' '2015-06-14' '2015-04-08' '2015-04-16' '2015-04-25'
 '2015-04-28' '2015-05-08' '2017-09-06' '2016-02-28' '2015-12-09'
 '2015-12-14' '2017-09-09' '2017-09-02' '2017-08-24' '2017-08-30'
 '2017-09-03' '2017-09-04' '2017-09-05' '2017-09-07' '2017-09-08'
 '2017-09-10' '2017-09-12' '2017-09-14' '2015-04-30' '2015-04-21'
 '2015-04-05' '2015-03-13' '2015-05-05' '2015-03-29' '2015-06-10'
 '2015-04-27' '2014-10-17' '2015-01-20' '2015-02-17' '2015-03-10'
 '2015-03-23']
-------------------------------------------------
name
['Ernest Barnes' 'Andrea Baker' 'Rebecca Parker' ... 'Wesley Aguilar'
 'Caroline Conley MD' 'Ariana Michael']
-------------------------------------------------
email
['Ernest.Barnes31@outlook.com' 'Andrea_Baker94@aol.com'
 'Rebecca_Parker@comcast.net' ... 'Mary_Morales@hotmail.com'
 'MD_Caroline@comcast.net' 'Ariana_M@xfinity.com']
-------------------------------------------------
phone-number
['669-792-1661' '858-637-6955' '652-885-2745' ... '395-518-4100'
 '531-528-1017' '422-804-6403']
-------------------------------------------------
credit_card
['************4322' '************9157' '************3734' ...
 '************9170' '************6349' '************7959']
-------------------------------------------------

🧹️ Data cleaning:

Removed unwanted columns:

df.drop(['name','email','phone-number','credit_card'],axis=1,inplace=True)

In our dataset there is features name, email, phone-number and credit_card, company will never give his costumers personal information so we need to delete those columns.

Changed DataType:

df['reservation_status_date'] = pd.to_datetime(df['reservation_status_date'])

reservation_status_date was in object type converted to datetime.

df.dtypes

hotel                                     object
is_canceled                                int64
lead_time                                  int64
arrival_date_year                          int64
arrival_date_month                        object
arrival_date_week_number                   int64
arrival_date_day_of_month                  int64
stays_in_weekend_nights                    int64
stays_in_week_nights                       int64
adults                                     int64
children                                 float64
babies                                     int64
meal                                      object
country                                   object
market_segment                            object
distribution_channel                      object
is_repeated_guest                          int64
previous_cancellations                     int64
previous_bookings_not_canceled             int64
reserved_room_type                        object
assigned_room_type                        object
booking_changes                            int64
deposit_type                              object
agent                                    float64
company                                  float64
days_in_waiting_list                       int64
customer_type                             object
adr                                      float64
required_car_parking_spaces                int64
total_of_special_requests                  int64
reservation_status                        object
reservation_status_date           datetime64[ns]
dtype: object

Handling Missing Values:

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

• ## ★ there are 4 missing values in children column
• ## ★ there are 488 missing values in country column
• ## ★ there are 16340 missing values in agent column
• ## ★ there are 112593 missing values in company column

df.drop(['agent','company'],axis=1,inplace=True)

➥ In agent and company there is huge missing values we can't handle it so i have deleted those columns.

df['country'].value_counts()

PRT    48590
GBR    12129
FRA    10415
ESP     8568
DEU     7287
       ...  
DJI        1
BWA        1
HND        1
VGB        1
NAM        1
Name: country, Length: 177, dtype: int64

➥ As you can see most occurred country is PRT so i'm going to fill missing values with PRT and for children fill with median.

df['country'].fillna('PRT',inplace=True)
df['children'].fillna(df['children'].median,inplace=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                          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
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

Handling Outliers:

plt.figure(figsize=(15, 5))
sns.boxplot(x='adr',data=df,color='#ff1a1a')

<AxesSubplot:xlabel='adr'>

plt.figure(figsize=(12, 5))
sns.kdeplot(x="adr",data=df)

<AxesSubplot:xlabel='adr', ylabel='Density'>

➥ As you can see data is right skweed and there is big outlier present.

df = df[df['adr']<5000]

plt.figure(figsize=(15, 5))
sns.boxplot(x='adr',data=df,color='#ff1a1a')

<AxesSubplot:xlabel='adr'>

⏳ Data Transformation:

df['is_canceled'].value_counts()

0    75166
1    44223
Name: is_canceled, dtype: int64

➥ AS you can see in is_canceled column there is two category 0 means Not Cancelled and 1 means Cancelled.

df['is_canceled'].replace([0,1],['Not_Cancelled','Cancelled'],inplace=True)

➥ Data transformed 0 to Not_Cancelled and 1 to Cancelled.

📊 Data Analysis and Visualization

➢ Reservation Status:

f,ax=plt.subplots(1,1,figsize=(15,8))
sns.countplot(x=df['is_canceled'],data=df,palette = 'bright',saturation=0.95)
ax.set_title('Reservation Status Count:',color='black',size=25)
for container in ax.containers:
    ax.bar_label(container,color='black',size=20)

➢ Reservation Status in Hotels:

f,ax=plt.subplots(1,1,figsize=(15,8))
sns.countplot(x=df['hotel'],data=df, hue='is_canceled',palette = 'flare',saturation=0.95)
ax.set_title('Reservation Status in different hotels',color='black',size=25)
plt.legend(['Not Cancelled','Cancelled'],fontsize=20)
plt.xlabel('Hotel',color='black',size=20)
plt.ylabel('Number of reservation',color='black',size=20)
for container in ax.containers:
    ax.bar_label(container,color='black',size=20) 
plt.show()    

Resort_Hotel = df[df['hotel'] == 'Resort Hotel']
Resort_Hotel['is_canceled'].value_counts(normalize=True)

Not_Cancelled    0.722366
Cancelled        0.277634
Name: is_canceled, dtype: float64

City_Hotel = df[df['hotel'] == 'City Hotel']
City_Hotel['is_canceled'].value_counts(normalize=True)

Not_Cancelled    0.582738
Cancelled        0.417262
Name: is_canceled, dtype: float64

➢ Average daily rate in City and Resort Hotel:

Resort_Hotel = Resort_Hotel.groupby('reservation_status_date')[['adr']].mean()
City_Hotel = City_Hotel.groupby('reservation_status_date')[['adr']].mean()

f,ax=plt.subplots(1,1,figsize=(20,8))
sns.lineplot( x=Resort_Hotel.index, y=Resort_Hotel['adr'])
sns.lineplot( x=City_Hotel.index, y=City_Hotel['adr'])
ax.set_title('Average daily rate in City and Resort Hotel',color='black',size=25)
plt.xlabel('Reservation date',color='black',size=20)
plt.ylabel('ADR',color='black',size=20)
plt.legend(['Resort_Hotel','City_Hotel'],fontsize=20)

<matplotlib.legend.Legend at 0x254cdb5f280>

• ### ➨ City hotel price is less as compare to Resort hotel for some days.
• ### ➨ As you can some spickes are there those are weekend or holidays.
• ### ➨ first hypothesis is correct

➢ Reservation Per Months:

df['month'] = df['reservation_status_date'].dt.month_name()

f,ax=plt.subplots(1,1,figsize=(15,8))
sns.countplot(x=df['month'],data=df, hue='is_canceled',palette = 'Blues',saturation=0.95)
ax.set_title('Reservation Status per month',color='black',size=25)
plt.legend(['Not Cancelled','Cancelled'],fontsize=15)
plt.xlabel('Months',color='black',size=20)
plt.ylabel('Number of reservation',color='black',size=20) 
plt.show() 

• ### ★ Most cancellation happend in january and less in august
• ### ★ Highest Reservation in august and less is December

• ### ➥ When my cancellation is less at that time Reservation is high (august)
• ### ➥ When my cancellation is high at that time Reservation is less (january)
• ### ➥ It seems like in august month hotel price was less so cancellation is less and Reservation is high
• ### ➥ Tt seems like in january month hotel price was high so cancellation is high and Reservation is less

➢ Average daily Rate Per Months when Cancelled:

f,ax=plt.subplots(1,1,figsize=(15,8))
sns.countplot(x=df['month'],data=df[df['is_canceled'] == 'Cancelled'].groupby('month')[['adr']].sum().reset_index(),palette = 'flare',saturation=0.95)
ax.set_title('ADR per month when Reservation Cancelled',color='black',size=25)
plt.xlabel(' Month',color='black',size=20)
plt.ylabel('ADR',color='black',size=20)
for container in ax.containers:
    ax.bar_label(container,color='black',size=20) 
plt.show() 

➢ Top 10 country where Cancellation is more :

Cancelled_data = df[df['is_canceled'] == 'Cancelled']
f,ax=plt.subplots(1,1,figsize=(15,8))
plt.pie(x = Cancelled_data['country'].value_counts().head(10),labels=Cancelled_data['country'].value_counts().head(10).index,autopct='%1.1f%%',shadow=True,explode=[0.1,0,0,0,0,0,0,0,0,0])
ax.set_title('Top 10 country in terms of Cancellation',color='black',size=25)
plt.show()

• ## ➥ Most Cancellation happend in PRT (Portugal) 70%.

➢ Top Reservation Channels:

df['market_segment'].value_counts()

Online TA        56477
Offline TA/TO    24218
Groups           19811
Direct           12606
Corporate         5295
Complementary      743
Aviation           237
Undefined            2
Name: market_segment, dtype: int64

df['market_segment'].value_counts(normalize=True)

Online TA        0.473050
Offline TA/TO    0.202850
Groups           0.165937
Direct           0.105588
Corporate        0.044351
Complementary    0.006223
Aviation         0.001985
Undefined        0.000017
Name: market_segment, dtype: float64

f,ax=plt.subplots(1,1,figsize=(15,8))
sns.countplot(x=df['market_segment'],data=df,order=df['market_segment'].value_counts().head(10).index,palette = 'bright',saturation=0.95)
ax.set_title('Reservation Channels',color='black',size=25)
plt.xlabel('Channels',color='black',size=20)
plt.ylabel('Number of reservation',color='black',size=20)
for container in ax.containers:
    ax.bar_label(container,color='black',size=20) 
plt.show()

• ## ➥ The most reservation came through online ~47%.

➢ Average Daily Rate when reservation Cancelled and Not Cancelled:

Not_Cancelled_data = df[df['is_canceled'] == 'Not_Cancelled']
Not_Cancelled_data_adr = Not_Cancelled_data.groupby('reservation_status_date')[['adr']].mean()
Cancelled_data_adr = Cancelled_data.groupby('reservation_status_date')[['adr']].mean()

f,ax=plt.subplots(1,1,figsize=(20,8))
sns.lineplot( x=Not_Cancelled_data_adr.index, y=Not_Cancelled_data_adr['adr'])
sns.lineplot( x=Cancelled_data_adr.index, y=Cancelled_data_adr['adr'])
ax.set_title('Average Daily Rate',color='black',size=25)
plt.xlabel('Reservation date',color='black',size=20)
plt.ylabel('ADR',color='black',size=20)
plt.legend(['Not Cancelled','Cancelled'],fontsize=20)

<matplotlib.legend.Legend at 0x254c7b463d0>