Abstract

In the highly competitive hospitality industry, predicting hotel booking cancellations can significantly improve operational efficiency and customer satisfaction. This project aims to develop a predictive model for hotel booking cancellations using machine learning algorithms. The study utilizes a comprehensive dataset containing various features such as booking lead time, customer demographics, booking channel, and historical booking data. The primary objective is to identify patterns and trends that influence cancellation behavior and develop a robust model that can accurately predict the likelihood of a booking being canceled.

Several machine learning algorithms were employed in this study, including Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM). The performance of these models was evaluated using standard metrics such as accuracy, precision, recall, F1 score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Feature importance analysis was also conducted to determine the most influential factors contributing to cancellations.

The results indicate that machine learning models can effectively predict booking cancellations, providing valuable insights for hotel management. By implementing these predictive models, hotels can enhance their revenue management strategies, reduce operational costs, and improve resource allocation. Furthermore, understanding the key drivers of cancellations can help in designing targeted interventions to reduce the likelihood of cancellations, thereby improving overall customer satisfaction.

Index Terms

• Hotel Booking
• Cancellation Prediction
• Machine Learning
• Logistic Regression
• Random Forest
• Gradient Boosting
• Support Vector Machine (SVM)
• Predictive Modeling
• Feature Importance
• Revenue Management
• Customer Satisfaction
• Operational Efficiency

Conclusion

The predictive modeling of hotel booking cancellations using machine learning algorithms has demonstrated significant potential in enhancing the operational efficiency and revenue management strategies of the hospitality industry. Through the application of Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM), we have developed models that can accurately predict the likelihood of booking cancellations. The evaluation metrics, including accuracy, precision, recall, F1 score, and AUC-ROC, indicate the efficacy of these models in identifying patterns and trends that influence cancellation behavior.

The feature importance analysis has highlighted critical factors contributing to cancellations, offering valuable insights for hotel management to develop targeted interventions. By understanding these key drivers, hotels can implement proactive measures to mitigate the risk of cancellations, leading to improved resource allocation, reduced operational costs, and enhanced customer satisfaction.

Overall, this study underscores the importance of leveraging machine learning techniques in the hospitality industry to address the challenges associated with booking cancellations. The implementation of predictive models not only aids in optimizing revenue management but also in crafting personalized customer experiences, thereby fostering long-term customer loyalty. Future research could explore the integration of additional data sources and the application of advanced algorithms to further refine and enhance the predictive capabilities of these models.