Forecasting hotel cancellations through Machine Learning

The analysis of tourist accommodation bookings provides valid information for the management of these establishments. The objective of this work is to analyze the performance of different Machine Learning techniques for the prediction of booking cancellations, as well as to analyze possible patterns in the study data. For this purpose, the following supervised learning methods are used: Multilayer Perceptron Neural Network, Radial Basis Function Neural Network, Decision Tree, Random Forest, AdaBoost and XgBoost, analyzing the performance of these techniques. The dataset used corresponds to the bookings of a resort hotel and a city hotel located in Portugal. As a result, the study compares the classification methods applied and identifies those with better performance, proving that Machine Learning techniques generate reliable forecasts for the management of the tourism industry.