Hotel Cancellation Rate Prediction: A Machine Learning Based Prediction Model

DOI

10.2991/978-94-6463-823-3_31How to use a DOI?

Keywords

Machine learning; hotel cancellation rate; ensemble learning; ROC-AUC curve

Abstract

With the advent of the digital age, people can book hotels remotely using mobile applications without having to visit in person, resulting in a surge in hotel cancellation rates, causing serious losses to hotels. Therefore, hotels must prepare in advance by predicting hotel cancellation rates. This study addresses the growing impact of hotel booking cancellations on inventory management and revenue planning by leveraging machine learning on large-scale booking data. After comprehensive data cleaning, feature engineering, and model comparisons, ensemble learning methods—particularly XGBoost—emerged as the best model, achieving an ROC-AUC of 0.9808 and an accuracy of 98.48%. Key predictive features include lead time, booking channel, and special requests. These findings demonstrate that advanced ensemble techniques significantly outperform traditional models, providing hotels with effective, data-driven tools for optimizing pricing strategies and resource allocation. Future work can focus on expanding data sources, enhancing feature selection techniques, and exploring alternative modeling approaches to further improve prediction accuracy.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Ziyue Luo
PY  - 2025
DA  - 2025/08/31
TI  - Hotel Cancellation Rate Prediction: A Machine Learning Based Prediction Model
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 318
EP  - 327
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_31
DO  - 10.2991/978-94-6463-823-3_31
ID  - Luo2025
ER  -