Hotel Booking Cancellation with Visual Analytics

Last-minute hotel reservation cancellations, often due to schedule changes, significantly impact hotel revenue. We can predict these cancellations by analyzing booking parameters such as the number of guests, rooms booked, and booking duration. Our study employed four models: Logistic Regression, SVM (Linear and RBF kernels), Decision Tree, and Random Forest. We evaluated these models using training and test datasets, with the Random Forest model achieving the highest accuracy at 99%. Additionally, we introduced a weighted average technique to help users visualize model performance and select the most suitable model for their needs.