Hotel Booking Demand

This data set contains booking information for a city hotel and a resort hotel, and includes information such as when the booking was made, length of stay, the number of adults, children, and/or babies, and the number of available parking spaces, among other things. All personally identifying information has been removed from the data.

We will perform exploratory data analysis with python to get insight from the data.

This article on medium explains the entire the process
Exploratory Data Analysis of the Hotel Booking Demand with Python

Table of Content

• Motivation
• Tools and Libraries Used
• Files
• Result
• Dataset Description
• Acknowledgements

Motivation

We have tried to answer the following Questions

1. How Many Booking Were Cancelled?
2. What is the booking ratio between Resort Hotel and City Hotel?
3. What is the percentage of booking for each year?
4. Which is the most busy month for hotel?
5. From which country most guest come?
6. How Long People Stay in the hotel?
7. Which was the most booked accommodation type (Single, Couple, Family)?

After that we made the predictive model to predict whether the booking will be cancelled or not

We will:

• Perform the Feature Engineering to make new featuers
• Perform the Data Selection to select only relevant features
• Tranform the Data (Categorial to Numerical)
• Split the data (Train Test Split)
• Model the data (Fit the Data)
• And finally Evaluate our model

Tools and Libraries Used

We have used Python 3 to its following packages:

• Pandas
• Matplotlib
• Seaborn
• Sklearn

Files

This repository contains two files other than readme file

Azzubair Assessment 12032022.ipynb: Jupyter Notebook file contains all the python code, documentation and visualization
hotel_bookings.csv: Our dataset file

Dataset Description

After going through with the original dataset documentation , I tabulated the information into the following table:

Table 1: Variables description

Attribute	Description	Notes	Data Type
hotel	Hotel Type	Resort Hotel City Hotel	Categorical
is_canceled	Value indicating if the booking was canceled	(1) is cancelled (0) is not cancelled	Categorical
lead_time	Number of days that elapsed between the entering date of the booking into the PMS and the arrival date	-	Integer
arrival_date_year	Year of arrival date	-	Integer
arrival_date_month	Month of arrival date with 12 categories	“January” to “December”	Categorical
arrival_date_week_number	Week number of the arrival date	-	Integer
arrival_date_day_of_month	Day of the month of the arrival date	-	Integer
stays_in_weekend_nights	Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel	-	Integer
stays_in_week_nights	Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel	-	Integer
adults	Number of adults	-	Integer
children	Number of children	-	Integer
babies	Number of babies	-	Integer
meal	Type of meal booked. Categories are presented in standard hospitality meal packages	Undefined/SC – no meal package BB – Bed & Breakfast HB – Half board (breakfast and one other meal – usually dinner) FB – Full board (breakfast, lunch and dinner)	Categorical
country	Country of origin. Categories are represented in the ISO 3155–3:2013 format	-	Categorical
market_segment	Market segment designation	“TA” = “Travel Agents” “TO” = “Tour Operators”	Categorical
distribution_channel	Booking distribution channel	“TA” = “Travel Agents” “TO” means “Tour Operators”	Categorical
is_repeated_guest	Value indicating if the booking name was from a repeated guest	(1) is repeated (0) is not repeated	Categorical
previous_cancellations	Number of previous bookings that were cancelled by the customer prior to the current booking	-	Integer
previous_bookings_not_canceled	Number of previous bookings not cancelled by the customer prior to the current booking	-	Integer
reserved_room_type	Code of room type reserved	Code is presented instead of designation for anonymity reasons	Categorical
assigned_room_type	Code for the type of room assigned to the booking	Sometimes the assigned room type differs from the reserved room type due to hotel operation reasons (e.g. overbooking) or by customer request. Code is presented instead of designation for anonymity reasons	Categorical
booking_changes	Number of changes/amendments made to the booking from the moment the booking was entered on the PMS until the moment of check-in or cancellation	-	Integer
deposit_type	Indication on if the customer made a deposit to guarantee the booking. This variable can assume three categories	No Deposit – no deposit was made Non Refund – a deposit was made in the value of the total stay cost Refundable – a deposit was made with a value under the total cost of stay	Categorical
agent	ID of the travel agency that made the booking	-	Categorical
company	ID of the company/entity that made the booking or responsible for paying the booking	ID is presented instead of designation for anonymity reasons	Categorical
days_in_waiting_list	Number of days the booking was in the waiting list before it was confirmed to the customer	-	Integer
customer_type	Type of booking, assuming one of four categories	Contract - when the booking has an allotment or other type of contract associated to it Group – when the booking is associated to a group Transient – when the booking is not part of a group or contract, and is not associated to other transient booking Transient-party – when the booking is transient, but is associated to at least other transient booking	Categorical
adr	Average Daily Rate	-	Numeric
required_car_parking_spaces	Number of car parking spaces required by the customer	-	Integer
total_of_special_requests	Number of special requests made by the customer (e.g. twin bed or high floor)	-	Integer
reservation_status	Reservation last status, assuming one of three categories	Canceled – booking was canceled by the customer Check-Out – customer has checked in but already departed No-Show – customer did not check-in and did inform the hotel of the reason why	Categorical
reservation_status_date	Date at which the last status was set	This variable can be used in conjunction with the ReservationStatus to understand when was the booking canceled or when did the customer checked-out of the hotel	Date

Result

From the dataset distribution, I found out that for each attribute:

1. hotel = City hotel is booked more than Resort hotel
2. is_canceled = Most booking are not cancelled, however cancelled booking are quite a lot too
3. lead_time = Most booking are done near or on the date of arrival and most of them are transient customer
4. arrival_date_year = customer arrived for the booking increased from 2015 until 2016 and slightly decreased on 2017
5. arrival_date_month = customer arrived for the booking are the highest during Summer season, and lowest during Winter season
6. arrival_date_week_number = People arrived for the booking are the highest on week 33 and lowest on week 52
7. arrival_date_day_of_month = Most customer arrived for the booking in the middle of the month
8. stays_in_weekend_nights = Most customer do not stay on weekend nights, but the are some people who would stay for 1-2 days.
9. stays_in_week_nights = People tend to stay mostly for 2 days during weekdays for City Hotel, while for Resort Hotel, people mostly stay for 1 until 3 days.
10. adults = Most people booking for 2 adults (probably newlyweds, or retirees), or 1 adult (probably single or a traveler)
11. children = Most customer did not bring any child for the hotel booking
12. babies = Most customer did not bring any baby for the hotel booking
13. meal = Most customer booked for BB (Bed & Breakfast) followed by HB (Half board) and SC (no meal package)
14. country = For City Hotel most people who booked are from Portugal, France, and Germany. Meanwhile for Resort Hotel, Most people who booked are from Portugal, United Kingdom, and Spain.
15. market_segment = Most customer booked using Online Tour Agent, Offline Tour Agent, and Tour Operators. Nowadays, people rarely use complementary market segment for hotel booking
16. distribution_channel = Most customer booked using Tour Agents, and Tour Operators, followed by Direct bookings and Corporate booking
17. is_repeated_guest = Most of the bookings are by non-repeated guests. Less than 5% of them are repeated guests.
18. previous_cancellations = Most customer did not cancel their previous booking prior to the current booking. Less than 3% of them cancelled their bookings prior to the current booking.
19. previous_bookings_not_canceled = Most customer did not cancel their previous booking prior to the current booking. Less than 3% of them cancelled their bookings prior to the current booking.
20. reserved_room_type = Customers mostly in favour for room type A, followed by D and others for both hotels
21. assigned_room_type = In the booking, most customers are assigned for room type A, followed by D and others for both hotels
22. booking_changes = Most booking did not make any booking changes. Only 20% of them does
23. deposit_type = Most people booking for no deposit, while some booking for non-refundable deposit. It is rare for customer to book for refundable deposit.
24. agent = Most customers used Agent ID no 9 for City Hotel booking and Agent ID No 240 for Resort Hotel booking
25. company = Most customers used Company ID no 40 for City Hotel booking and Company ID No 223 for Resort Hotel booking
26. days_in_waiting_list = Most customers who booked are not in waiting list. Less than 5% of them are from waiting list
27. customer_type = Most customers are transient who are usually walk-in guests, last minute or bookers or simply people that require a very short term stay in the hotel
28. required_car_parking_spaces = Most customers did not require parking spaces. Less than 2% requires parking spaces.
29. total_special_requests = Most customers did not have special request. However those who have special request quite a lot too
30. reservation_status = City hotel has more checkout reservation status than Resort hotel. However, City Hotel also has the most cancellation reservation status than Resort hotel.

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. The data was downloaded and cleaned by Thomas Mock and Antoine Bichat for #TidyTuesday during the week of February 11th, 2020.