Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)

Comparison and Analysis of Machine Learning Models to Predict Hotel Booking Cancellation

Authors
Yiying Chen1, Chuhan Ding2, Hanjie Ye3, Yuchen Zhou4, *
1Management School, Beijing Jiaotong University, Beijing, China
2Thurgood Marshall College, University of California San Diego, U. S. A.
3School of Social Science, University of California Irvine, U. S. A.
4School of Economics, Huazhong University of Science and Technology, Wuhan, China
*Corresponding author. Email: u201916417@hust.edu.cn
Corresponding Author
Yuchen Zhou
Available Online 26 March 2022.
DOI
https://doi.org/10.2991/aebmr.k.220307.225How to use a DOI?
Keywords
CatBoost; hospitality industry; machine learning models; prediction
Abstract

Hotel booking cancellation prediction is crucial in conducting revenue and resource management for hotels. This paper provides three possible substitutes for the neural network including logistic regression, k-Nearest Neighbor (k-NN), and CatBoost, whereas CatBoost, is the most suitable model for hotels to do the prediction. The advantages of them are effectiveness, high accuracy, and lower cost. The dataset used in this paper was adapted from Kaggle, a set of the booking data from two types of hotels (resort hotel and city hotel) in Portugal, and the corresponding customers’ information. We select some key variables as the predictor to train and test the prediction models based on three machine learning algorithms. After preprocessing the raw data, i.e., standardizing, dealing with missing data, recoding some variables, and scaling, we conduct the prediction and compare each model through three metrics (confusion matrix, accuracy score, and F1-score). The result indicates that CatBoost has the best performance in predicting hotel booking cancellation because it has the greatest number of correct prediction samples and the highest accuracy score. We focus on the efficiency and economy of doing cancellation prediction in the hospitality industry to form a basis for future revenue and resource management for hotels.

Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

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Volume Title
Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)
Series
Advances in Economics, Business and Management Research
Publication Date
26 March 2022
ISBN
978-94-6239-554-1
ISSN
2352-5428
DOI
https://doi.org/10.2991/aebmr.k.220307.225How to use a DOI?
Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yiying Chen
AU  - Chuhan Ding
AU  - Hanjie Ye
AU  - Yuchen Zhou
PY  - 2022
DA  - 2022/03/26
TI  - Comparison and Analysis of Machine Learning Models to Predict Hotel Booking Cancellation
BT  - Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)
PB  - Atlantis Press
SP  - 1363
EP  - 1370
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220307.225
DO  - https://doi.org/10.2991/aebmr.k.220307.225
ID  - Chen2022
ER  -