Hotel Booking Cancellation Prediction Using Applied Bayesian Models
This addresses booking management challenges for the hotel industry, but it is incremental as it applies existing Bayesian methods to a specific domain.
This study tackled hotel booking cancellation prediction using Bayesian models on a Kaggle dataset, finding that a Bayesian Logistic Regression model outperformed a Beta-Binomial model in accuracy, with special requests and parking availability as the strongest predictors.
This study applies Bayesian models to predict hotel booking cancellations, a key challenge affecting resource allocation, revenue, and customer satisfaction in the hospitality industry. Using a Kaggle dataset with 36,285 observations and 17 features, Bayesian Logistic Regression and Beta-Binomial models were implemented. The logistic model, applied to 12 features and 5,000 randomly selected observations, outperformed the Beta-Binomial model in predictive accuracy. Key predictors included the number of adults, children, stay duration, lead time, car parking space, room type, and special requests. Model evaluation using Leave-One-Out Cross-Validation (LOO-CV) confirmed strong alignment between observed and predicted outcomes, demonstrating the model's robustness. Special requests and parking availability were found to be the strongest predictors of cancellation. This Bayesian approach provides a valuable tool for improving booking management and operational efficiency in the hotel industry.