IDEAS home Printed from https://ideas.repec.org/a/pal/jorapm/v24y2025i6d10.1057_s41272-025-00532-x.html
   My bibliography  Save this article

Predicting hotel booking cancellations: a comprehensive machine learning approach

Author

Listed:
  • Apostolos Ampountolas

    (Boston University School of Hospitality Administration)

Abstract

Research on predicting booking cancellation behavior remains limited despite its operational importance. This study evaluates machine learning models based on 209,545 reservations (including 46,070 cancellations) from a four-star hotel chain (from 2015 to 2019). While XGBoost and Ridge Regression achieve high precision (97.65% and 95.65%), their high false negative rates and low recall limit sensitivity-critical applications. Conversely, KNN and a hybrid XGBoost-Ridge model excel in recall and minimize false negatives, ensuring robust cancellation prediction. Naive Bayes balances precision and recall effectively. The findings emphasize diverse metrics for model evaluation and advanced hybrid modeling for actionable insights in hotel revenue management.

Suggested Citation

  • Apostolos Ampountolas, 2025. "Predicting hotel booking cancellations: a comprehensive machine learning approach," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 24(6), pages 539-550, December.
    • Handle: RePEc:pal:jorapm:v:24:y:2025:i:6:d:10.1057_s41272-025-00532-x
    DOI: 10.1057/s41272-025-00532-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41272-025-00532-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/s41272-025-00532-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorapm:v:24:y:2025:i:6:d:10.1057_s41272-025-00532-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.