Hotel Booking Cancellation Prediction by Feature Interaction and Machine Learning

Abstract

With the rise of online travel platforms (e.g., Ctrip, Qunar), modifying or canceling hotel reservations has become more convenient, which in turn increases the probability of reservation cancellations. Each cancellation imposes issues on hotels, such as higher room vacancy rates, unstable revenue, and increased operating costs, presenting a significant challenge to revenue management teams. Only by accurately predicting cancellations can effective strategies be formulated to mitigate such losses. To address this problem, this paper enriches existing hotel reservation information by applying feature interaction technology. Subsequently, we compare the performance of machine learning models, including Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB), and selects the optimal model based on evaluation results. Through the methods, hotels can proactively identify the likelihood of reservation cancellations, thereby developing more targeted strategies to reduce potential losses.