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Original Article

A Comparative Study of Machine Learning and Deep Learning Approaches For Hotel Booking Cancellation Prediction

Taha Yiğit Alkan1
Graduate School of Natural and Applied Sciences, Akdeniz University, Türkiye.

Published Online: May-June 2025

Pages: 11-18

References

1. Chen, H. Machine Learning for Hotel Reservation Prediction.
2. ZHANG, Y. C. HOTEL BOOKING CANCELLATION PREDICTION: A COMPARISON BETWEEN MACHINE LEARNING ALGORITHMS
AND NEURAL NETWORKS (Doctoral dissertation, tilburg university).
3. Lee, H. A., Denizci Guillet, B., & Law, R. (2013). An examination of the relationship between online travel agents and hotels: A case study
of Choice Hotels International and Expedia. com. Cornell Hospitality Quarterly, 54(1), 95-107.
4. Chen, S., Ngai, E. W., Ku, Y., Xu, Z., Gou, X., & Zhang, C. (2023). Prediction of hotel booking cancellations: Integration of machine learning
and probability model based on interpretable feature interaction. Decision Support Systems, 170, 113959.
5. Lau, G., & Kerimov, A. (2025). Hotel reservation cancellations: predictive modeling and feature impact analysis. Journal of High School
Science, 9(1), 303-320.
6. Antonio, N., de Almeida, A., & Nunes, L. (2017, December). Predicting hotel bookings cancellation with a machine learning classification
model. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 1049-1054). IEEE.
7. Herrera, A., Arroyo, A., Jiménez, A., & Herrero, Á. (2024). Forecasting hotel cancellations through machine learning. Expert Systems,
41(9), e13608.
8. Gartvall, E., & Skånhagen, O. (2022). Predicting hotel cancellations using machine learning.
9. Bhardwaj, A., Yadav, T., & Chaudhary, R. (2024, June). Predicting Hotel Booking Cancellations using Machine Learning Techniques.
In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
10. Arik, S. Ö., & Pfister, T. (2021, May). Tabnet: Attentive interpretable tabular learning. In Proceedings of the AAAI conference on artificial
intelligence (Vol. 35, No. 8, pp. 6679-6687).
11. Ahsan81. (2021). Hotel reservations classification dataset [Data set]. Kaggle. https://www.kaggle.com/datasets/ahsan81/hotel-reservations-
classification-dataset
12. Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international
conference on knowledge discovery and data mining (pp. 785-794).
13. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., ... & Chintala, S. (2019). PyTorch: An imperative style, high-
performance deep learning library. In Advances in Neural Information Processing Systems, 32
14. OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://openai.com/chatgpt
15. Grammarly Inc. (n.d.). Grammarly [Software]. https://www.grammarly.com/

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