Hotel Booking Cancellation Prediction Using Applied Bayesian Models

TL;DR

This paper demonstrates the application of Bayesian models, specifically Bayesian Logistic Regression, to predict hotel booking cancellations, improving operational efficiency and resource management in hospitality.

Contribution

It introduces a Bayesian modeling approach for cancellation prediction, highlighting key predictors and validating model robustness with LOO-CV, which is novel in this context.

Findings

Bayesian Logistic Regression outperforms Beta-Binomial model in accuracy.

Key predictors include special requests and parking availability.

Model validation confirms strong predictive performance.

Abstract

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.