Leveraging Machine Learning for Sustainable Hotel Management: Predicting Booking Cancellations to Optimize Operations

This paper explores the application of machine learning (ML) techniques to predict hotel booking cancellations in the UK, addressing a critical issue affecting the hospitality industry's financial performance and operational efficiency. By examining eight distinct ML models, this research aims to provide insights into how data-driven predictions can significantly mitigate revenue losses and enhance booking management. The study analyses a comprehensive dataset of UK hotel bookings, identifying key factors that contribute to cancellations. By leveraging advanced ML algorithms, the research forecasts cancellation likelihood with notable accuracy and provides a strategic framework for hotels to refine their customer service approaches. The integration of sustainable hotel management practices is emphasized, showcasing how accurate cancellation predictions can improve operational efficiency, reduce overbooking risks, and enhance customer satisfaction. The findings underscore the importance of predictive analytics in crafting more resilient and customer-centric business strategies within the hospitality industry. Additionally, the paper discusses the broader implications of these technological applications, suggesting avenues for future research and the potential extension of these models to other sectors. This study highlights the dual role of ML technology in addressing immediate operational challenges and enhancing the overall customer experience, thereby contributing to the sustained success and growth of the hospitality industry in the UK and beyond.