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Predictive models for hotel booking cancellation
(11/10/2023) Arseniou, Evangelia; Αρσενίου, Ευαγγελία; Athens University of Economics and Business, Department of Management Science and Technology; Karlis, Dimitrios; Chatziantoniou, Damianos; Ntzoufras, Ioannis
Room cancellations pose a big challenge to the hotel industry, as the number of guests directly affects the entire operational framework. The primary purpose of the thesis is to predict hotel booking cancellations using machine learning techniques and analyze the most influential factors driving these cancellations. The problem of predicting cancellations is a binary classification problem, classifying outcomes as either cancellations or non-cancellations. The data are sourced from two hotels in Portugal, including a city hotel and a resort hotel. The implementation of machine learning algorithms, specifically Decision Trees, Random Forest, and Logistic Regression, was conducted using the R programming language. The key findings highlight Random Forest as the best-performing model, achieving an accuracy of 85% on the hotel dataset. The lead time was the most influential, followed by region and total of special requests. Additionally, Multiple Linear Regression was used to predict the guests’ duration of stay. However, the observed low adjusted R-squared value and the violation of residual assumptions indicate limited predictive efficacy. The final phase of the research involved applying K-means clustering for customer segmentation, a pivotal step in making effective business decisions. The resulting analysis yielded four distinct clusters, each offering valuable insights into customer behavior and preferences.

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