Prediction of hotel bookings cancellation using hyperparameter optimization on Random Forest algorithm

Abstract

Cancellation of hotel bookings by customers greatly influences hotel managerial decision making. To minimize losses by this problem, the hotel management made a fairly rigid policy that could damage the reputation and business performance. Therefore, this study focuses on solving these problems using machine learning algorithms. To get the best model performance, hyperparameter optimization is applied to the random forest algorithm. It aims to obtain the best combination of model parameters in predicting hotel booking cancellations. The proposed model is proven to have the best performance with the highest accuracy results of 87 %. This study's results can be used as a model component in hotel managerial decision-making systems related to future bookings' cancellation.