Citation

Kannan, Rathimala and Bavaharini, K. and Murugasamy, Manoj and Yiin, Woo Kah and Wijaya, Rita (2025) Predicting Reservation Cancellations with Machine Learning: A Case Study of a 3-Star Hotel. In: Lecture Notes in Networks and Systems, 22 November 2024 - 24 November 2024, Bhopal.

Text 10.1007/978-981-96-5784-1_21 - Published Version Restricted to Repository staff only Download (275kB)

Abstract

The hotel industry, closely tied to the tourism sector, plays a critical role in supporting national economies by offering a range of accommodations to guests. Effective hotel management, encompassing finance, marketing, distribution and human resources, is essential for ensuring steady growth. However, a persistent challenge in the industry is managing reservation cancellations, which significantly impact revenue and the accuracy of demand forecasting. One of the main issues is the ignorance of what causes cancellations and how to predict them. In order to address this gap, this study uses machine learning algorithms to predict cancellations of hotel reservations. The research uses the CRISP-DM framework to assess and predict cancellations using a real-world customer transaction dataset from a 3-Star Hotel in Indonesia. Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree and Random Forests are among the machine learning techniques that are tested in this study. With an F-measure of 94.60% among them, the Random Forest model showed the best prediction ability. This research gives hoteliers a mechanism to better predict cancellation patterns and modify cancellation and overbooking rules to minimize costs by utilizing machine learning.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	ANN, Booking cancellation prediction, classification models, decision Tree, random forest, SVM
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Management (FOM)
Depositing User:	Nurin Syazwani Azmi
Date Deposited:	06 Nov 2025 06:37
Last Modified:	06 Nov 2025 06:37
URII:	http://shdl.mmu.edu.my/id/eprint/14721

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