Detecting Short-Notice Cancellation in Hotels with Machine Learning

Abstract

Cancellations play a critical role in the lodging industry. Considering the time horizon, cancellations placed close to check-in have a significant impact on hoteliers, who must respond promptly for effective management. In recent years, the introduction of personal name records (PNR) has brought innovative approaches to this domain, but short-notice cancellation prediction is still underdeveloped. Using real PNR data with more than 10k reservations provided by a four-star hotel, this research aims to combine fuzzy clustering with tree decision techniques and random forest under R software version 4.3.3 to forecast cancellations placed close to the entry day, slightly improving the performance of individual techniques.