Hotel Reservation Cancellation Prediction using Boosting Model

This research investigates the effectiveness of machine learning (ML) for predicting hotel cancellations, providing valuable insights for optimizing hotel management strategies. ML algorithms, including LightGBM, Gradient Boosting Classifier, AdaBoost Classifier, and CatBoost Classifier, consistently achieve exceptional accuracy of 99%, making them robust choices for cancellation prediction. While XGBoost demonstrates commendable performance with a 98% accuracy rate, the study underscores the importance of precision and recall metrics for effective decision-making in the hospitality sector. These findings guide decision-makers in the hotel industry towards informed choices in ML algorithms, emphasizing the nuanced trade-offs between precision, recall, and computational efficiency. The research significantly contributes to advancing predictive modelling in the hospitality sector, laying the groundwork for enhanced reservation systems and customer service practices in the hotel industry.