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Forecasting hotel cancellations through Machine Learning
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  • Anita Herrera,
  • Ángel Arroyo,
  • Alfredo Jimenez,
  • Álvaro Herrero
Anita Herrera
Universidad de Burgos Escuela Politecnica Superior

Corresponding Author:[email protected]

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Ángel Arroyo
Universidad de Burgos Escuela Politecnica Superior
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Alfredo Jimenez
Kedge Business School - Campus Bordeaux
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Álvaro Herrero
Universidad de Burgos Escuela Politecnica Superior
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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.
16 Apr 2023Submitted to Expert Systems
17 Apr 2023Assigned to Editor
17 Apr 2023Submission Checks Completed
07 May 2023Reviewer(s) Assigned
29 May 2023Review(s) Completed, Editorial Evaluation Pending
10 Aug 2023Editorial Decision: Revise Major