Performance Comparison of Classification Algorithms in Hotel Booking Cancellation Prediction

Abstract

Having a high occupancy rate is one of the most important goals of hotel management. However, booking cancellations have a negative effect on the profit rates of the hotels. Although hotel businesses try to develop various solutions to overcome this problem, they cannot achieve the desired result. In this context, it is of great importance for hotels to be able to predict booking cancellations that may occur. In order to solve this problem, in this study, k-Nearest Neighbors algorithm, Logistic Regression, Artificial Neural Networks, Decision Tree algorithm, Random Forest algorithm and Gradient Boosting algorithm are run on an open shared dataset that includes the reservation information of various hotels between 2015 and 2017. When the results are compared, it has been shown that K-Nearest Neighbors and Random Forest algorithms are the best solutions to the problem with both have 85% accuracy.

Keywords

• hotel booking
• classification algorithms
• cancellation prediction

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