Hotel Booking Prediction Using Machine Learning
Hotel Booking Prediction Using Machine Learning
Hotel Booking Prediction Using Machine Learning
https://doi.org/10.22214/ijraset.2022.43036
International Journal for Research in Applied Science & Engineering Technology (IJRASET)
ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538
Volume 10 Issue V May 2022- Available at www.ijraset.com
Abstract: In this Project ‘Hotel Booking Prediction’, an accurate booking cancellation forecast by which user know the things
related to hotel bookings very earlier. Booking cancellation has a significant effect on revenue which essentially affects request
the board choices in the inn business. To reduce the cancellation effect, the hotel applies the cancellation model as the key to
addressing this problem with the machine learning-based system developed. By combining data science tools and capabilities
with human judgement and interpretation, this project aims to demonstrate how the predictive analysis of the model can
contribute to synthesizing and predict about booking cancellation forecasting. Furthermore, this project aims, by detailing the
full prediction & analysis, to give relaxation to user who want to apply in particular hotel. By Implement Various Algorithms
like Logistic, KNN, Random Forest, Decision Tree, etc. to classify the data and also use Evaluation Matrix to separate
categorical data in particular type, user can know the prediction up to the desired level. It prevents the hotel as well as Tourists
to poor dealing of room. User/Customer have to enter certain field by which this model detects his prediction about the
cancellation.
Keywords: Logistic Regression, KNN, Random forest, Decision Tree, Evaluation Matrix.
I. INTRODUCTION
Hotels plays an important role for any person or traveler who are travelling from one destination to another. Hotel play an important
role for tourists whether the tourist is local or international. Hotel provides many best services to the customer such as parking area,
food, room service and also it provides services that is offered by customer. By providing these services Hotels take the valuable
feedback from the customers. By these feedbacks Hotels maintains their reputation in the city/area. If the services are poor, the
bookings of that hotel are low and if the services are awesome then high bookings in that hotel takes place.
In this model, the prediction possibility of a booking for a hotel based on different factors and also try to predict if they need special
requests based on different features. In this project the dataset which we are using contains both International and Local Hotel data.
Here we use many popular Machine Learning Algorithms like Decision Tree, Random Forest, KNN, Logistic Regression, etc. to
predict the cancellation chances. This Model gives the prediction of Hotel Booking Cancellation up to the certain level of accuracy
i.e. 95% (Approx.).
II. PROBLEM STATEMENT
1) Leverage Guest Data and Booking Behavior Patterns to devise a strategy for Hotel Booking Management, using Machine
Learning.
2) With the increasing day to day trend of hotel cancellation at border time, it affects the local as well as international tourists
more and more. Customer/Tourists don't have any idea about the pre-cancellation scenario of particular hotel at the time of
booking of that hotel.
Solution: By ‘HOTEL BOOKING PREDICTION’ user can know about the cancellation percentage of Hotels in very early time.
With the help of these predictions of Hotel Cancellation user can take decision about the booking of hotel/resort in Lead time or not,
also it will allow hotel managers / revenue manager to take actions on bookings that's identified as "potentially going to be
canceled", furthermore the development of these model should contribute to hotel revenue management.
©IJRASET: All Rights are Reserved | SJ Impact Factor 7.538 | ISRA Journal Impact Factor 7.894 | 4058
International Journal for Research in Applied Science & Engineering Technology (IJRASET)
ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538
Volume 10 Issue V May 2022- Available at www.ijraset.com
Evaluation Matrix
Logistic Regression KNN Decision Tree Random Forest XGB
Accuracy 0.804135 0.834391 0.831365 0.840316 0.832962
Recall 0.615210 0.744645 0.745665 0.763799 0.716083
Precision 0.810875 0.795592 0.788093 0.797043 0.811248
F1 Score 0.699620 0.769276 0.766292 0.780067 0.760701
Table – 1
Evaluation Matrix Of Differnet Algorithms
©IJRASET: All Rights are Reserved | SJ Impact Factor 7.538 | ISRA Journal Impact Factor 7.894 | 4059
International Journal for Research in Applied Science & Engineering Technology (IJRASET)
ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538
Volume 10 Issue V May 2022- Available at www.ijraset.com
Here in this model “GridSearchCV” is used for Hyperparameter Tuning. In GridSearchCV approach, machine learning model is
evaluated for a range of hyperparameter values. This approach is called GridSearchCV, because it searches for best set of
hyperparameters from a grid of hyperparameters values.
VII- CONCLUSIONS
Tuned Random Forest Has the Best Accuracy Among All Algorithm That We Tried from all the evaluation matrix to predict hotel
cancellation classification case, we see that Tuned Random Forest has the best accuracy when it comes to predicting hotel
cancellation based on certain features (85.2 %).
This model enables hotel managers to mitigate revenue loss derived from booking cancellations and to mitigate the risks associated
with overbooking (reallocation costs, cash or service compensations, and, particularly important today, social reputation costs). This
project also allows hotel managers to implement less rigid cancellation policies, without increasing uncertainty. This has the
potential to translate into more sales, since less rigid cancellation policies generate more bookings.
REFERENCES
[1] Predicting Hotel Bookings Cancellation with machine learning classification model, Nuno Antonio, Ann de Almeida, Luis Nunes, IEEE International
Conference on Machine Learning and Application, 2017.
[2] Application of Machine Learning in the Hotel Industry: A Critical Review, Dr. Eid Tourism, Archaeology Department, College of Arts, University of Hail,
P.O.Box 2440Hail, Saudi Arabia, 2020.
[3] A Grouping Hotel Recommender System Based on Deep Learning and Sentiment Analysis, Fatemeh Abbasi, Ameneh Khadivar, Mohsen Yazdinejad, 2019.
[4] Machine Learning for Web Page Adaptation, Neetu Narwal, Dr. Sanjay Kumar Sharma, 2016.
[5] Studying the cancellation behaviour of the guests, Markku Vieru , 2016
[6] https://medium.com/analytics-steps/defining-predictive-modeling-in-machine-learning-887c23b7a278
©IJRASET: All Rights are Reserved | SJ Impact Factor 7.538 | ISRA Journal Impact Factor 7.894 | 4060