Optimization of Prediction for Cancellation of Hotel Room Reservation Using Decision Tree with Feature Selection and Resampling

Abstract

The hotel industry is highly competitive and faces challenges, such as fluctuating demand, intense competition, and shifting consumer preferences. One critical issue that hotels frequently encounter is the cancellation of room reservations, which disrupts operational planning and resource management and leads to significant financial losses. Accurately predicting the likelihood of reservation cancellation is essential to mitigate these negative impacts and optimize revenue management strategies. This study focuses on the development of a predictive model for hotel room reservation cancellations using a decision-tree algorithm. The Decision Tree was selected for its ability to manage complex relationships between variables and ease of interpretation, making it accessible to hotel managers without technical expertise. To enhance the performance of the model, a forward selection technique was employed to identify the most relevant features, ensuring a balance between the model complexity and predictive accuracy. Additionally, resampling techniques were applied to address class imbalance in the dataset, which is common in cancellation cases where non-cancelled reservations outnumber cancelled reservations. This study explores the prediction of hotel room reservation cancellations using a decision tree algorithm enhanced by feature selection and resampling. The model achieved an accuracy improvement to 90%, with precision and recall each increasing by 5,5% after applying these techniques. These findings suggest practical applications for improving cancellation predictions and optimizing revenue management strategies for hotels. The study provides insights into how data-driven approaches can enhance decision-making processes within the competitive hospitality industry.