Booking cancellations have a momentous impact on the hospitality industry,
as regards to demand management. In order to diminish the influence of cancellations,
hotels apply severe cancellation policies and tactics, that may have negative results on
the hotel’s prestige and therefore its revenue. To minimize the impact of booking
cancellations and improve the functionality of the hotel, a machine learning based
model was developed. By using a dataset of a 4-stars hotel and approaching
cancellation prediction as a supervised anomaly detection concept, it is exhibited that
it is possible to develop a predicting machine learning model to forecast booking
cancellations with overall accuracy 99%. The results of the research give the
opportunity to the hotel manager to accurately predict demand through cancellations,
produce improved forecasts and define better overbooking strategies.
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