Exploratory Data Analysis(EDA) For Predicting Hotel Booking Cancellations Using Machine Learning
By: Chaithanya Vamshi
Tags: Eda, Python, Hospitality, Data Visualization, Machine Learning

Booking cancellations have a substantial impact on demand management decisions in the hospitality industry.

Every year, more than 140 million bookings were made on the internet and many hotel bookings were booked through top-visited travel websites like Booking.com, Expedia.com, Hotels.com, etc.

Hotel Booking Cancellations, A Growing Problem…

When Analyzing the past 5 years data, D-Edge Hospitality Solutions has found that the global average of cancellation rate on bookings has reached almost 40% and this trend produces a very negative impact on hotel revenue and distribution management strategies.

To overcome the problems caused by booking cancellations, hotels implement rigid cancellation policies, inventory management, and overbooking strategies, which can also have a negative influence on revenue and reputation.

Once the reservation has been canceled, there is almost nothing to be done and it creates discomfort for many Hotels and Hotel Technology companies. Therefore, predicting reservations which might get canceled and preventing these cancellations will create a surplus revenue for both Hotels and Hotel Technology companies.

Motivation

Have you ever wondered what if there was a way you could predict which guests were likely to cancel and adjust the overbook rate accordingly? That would be great right?

Luckily, by using Machine learning with Python, we can predict the guests who are likely to cancel the reservation and this could help produce better forecasts and reduce uncertainty in business decisions.

In this article, I will apply Exploratory Data Analysis (EDA) to get insights from the data set to know which features have contributed more in predicting cancellations by performing Data visualization with Matplotlib & Seaborn. It is always a good practice to understand the data first and try to gather as many insights from it.

Exploratory Data Analysis (EDA) with Data Visualization

To better understand the dataset, we have to come up with a list of questions.

What are the Top 10 Countries of Origin of Hotel visitors (Guests)

Around 40% of all bookings were booked from Portugal followed by Great Britain(10%) & France(8%).

2. Which Month is the Most Occupied with Bookings at the Hotel?

According to the graph, August is the most occupied (busiest) month with 11.62% bookings and January is the most unoccupied month with 4.96% bookings.

3. How many Bookings were Cancelled at the Hotel?

According to the pie chart, 63% of bookings were not canceled and 37% of the bookings were canceled at the Hotel.

4. Which Month Has Highest Number of Cancellations By Hotel Type?

For the City hotel, the number of cancelations per month is around 40 % throughout the year and for the Resort hotel, the cancellations are highest in June, July & August and lowest during November, December & January.

5. How many Bookings were Cancelled by Hotel Type?

For the Resort Hotel, a total of 25.14% Bookings were canceled and for the City Hotel, a total of 74.85% Bookings were canceled.

6. Relationship between Average Daily Rate(ADR) and Arrival Month by Booking Cancellation Status

The highest Average Daily Rate (ADR) has occurred in August and due to the highest ADR in August, maybe it could be one of the reasons for more cancelations in August.

7. Total Number of Bookings by Market Segment

Around 47% of bookings are made via Online Travel Agents, almost 20% of bookings are made via Offline Travel Agents and less than 20% are Direct bookings without any other agents.

8. Arrival Date Year vs Lead Time By Booking Cancellation Status

For all the 3 years, bookings with a lead time less than 100 days have fewer chances of getting canceled, and lead time more than 100 days have more chances of getting canceled.

9. Relationship between Special Requests and Cancellations

Around 28% of bookings were canceled with no special requests from the guests followed by 6% bookings were canceled with one special request from the guests.

10. How does the ADR Vary Over the Year by Hotel Type

For Resort Hotel, ADR is more expensive during July, August & September and for City Hotel, ADR is slightly more during March, April & May.

11. What is the Effect of Deposit Type on Cancellations

Around 28% of bookings were canceled by guests with no deposit , followed by 22% bookings were canceled with Refundable.

of bookings were canceled by guests with , followed by bookings were canceled with So it's obvious that guests who do not pay any deposit while booking are likely to cancel more reservations.

Business Summary

From our EDA, we have observed that the top 5 most important features in the data set which helps in predicting Cancellations from the guests are :

Lead Time ADR Deposit Type Arrival Day of the Month Total Number of Special Requests

Strategies to Counter High Cancellations at the Hotel

Set Non-refundable Rates, Collect deposits, and implement more rigid cancellation policies.

Using Advanced Purchase Rates with varying Lead Time windows

Encourage Direct bookings by offering special discounts

Hotels should consider the total number of special requests from guests to reduce the possibility of cancellations by improving customer service.

Monitor where the cancellations are coming from such as Market Segment, distribution channels, etc.

As always, I welcome feedback and constructive criticism. Python Code for Data Visualization is available on GitHub and I can also be reached on LinkedIn.

References