Predicting hotel booking cancelation with machine learning techniques

Myongjee Yoo

doi:10.1108/JHTT-07-2022-0227

Predicting hotel booking cancelation with machine learning techniques

2023-09-27

8

PRE

AI

DOI：10.1108/JHTT-07-2022-0227

Original

Original request for help

Save

Abstract

En 中文

Purpose- The purpose of this study is to develop a model that accurately forecasts hotel room cancelations and further determines the key cancelation drivers.Design/methodology/approach- Predictive modeling, specifically the machine learning methods, is used to forecast room cancelations and identify the main cancelation factors.Findings- By using three different classification algorithms, this study demonstrates that hotel room cancelation can be accurately predicted using XGBoost, as well as the ensemble method involving Support Vector Machine, Random Forest and XGBoost.Originality/value- This study attempted to forecast hotel room cancelations by applying a relatively new method, machine learning. By implementing predictive modeling, one of the most emerging and innovative research methods, this study ultimately provides prediction suggestions in various aspects and levels for hotel management operations.

Keywords:

Forecasting

Machine learning

Revenue management

Big data

Predictive modeling

Hotel booking cancelation

Predicting hotel booking cancelation with machine learning techniques

Abstract

Journal

Organization