Detecting Short-Notice Cancellation in Hotels with Machine Learning

Cancellations play a critical role in the lodging industry. Considering the time horizon, cancellations placed close to check-in have a significant impact on hoteliers, who must respond promptly for effective management. In recent years, the introduction of personal name records (PNR) has brought in...

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Main Authors: Eleazar C-Sánchez, Agustín J. Sánchez-Medina
Format: Article
Language:English
Published: MDPI AG 2024-07-01
Series:Engineering Proceedings
Subjects:
Online Access:https://www.mdpi.com/2673-4591/68/1/43
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author Eleazar C-Sánchez
Agustín J. Sánchez-Medina
author_facet Eleazar C-Sánchez
Agustín J. Sánchez-Medina
author_sort Eleazar C-Sánchez
collection DOAJ
description Cancellations play a critical role in the lodging industry. Considering the time horizon, cancellations placed close to check-in have a significant impact on hoteliers, who must respond promptly for effective management. In recent years, the introduction of personal name records (PNR) has brought innovative approaches to this domain, but short-notice cancellation prediction is still underdeveloped. Using real PNR data with more than 10k reservations provided by a four-star hotel, this research aims to combine fuzzy clustering with tree decision techniques and random forest under R software version 4.3.3 to forecast cancellations placed close to the entry day, slightly improving the performance of individual techniques.
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institution Kabale University
issn 2673-4591
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series Engineering Proceedings
spelling doaj-art-a3a81f7e6fbc48658f42880923d09bf12025-08-20T03:43:15ZengMDPI AGEngineering Proceedings2673-45912024-07-016814310.3390/engproc2024068043Detecting Short-Notice Cancellation in Hotels with Machine LearningEleazar C-Sánchez0Agustín J. Sánchez-Medina1Department of Communications, Faculty of Social and Legal Sciences, Mid-Atlantic University, 35017 Las Palmas de Gran Canaria, SpainUniversity Institute of Cybernetic, Business and Society, University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, SpainCancellations play a critical role in the lodging industry. Considering the time horizon, cancellations placed close to check-in have a significant impact on hoteliers, who must respond promptly for effective management. In recent years, the introduction of personal name records (PNR) has brought innovative approaches to this domain, but short-notice cancellation prediction is still underdeveloped. Using real PNR data with more than 10k reservations provided by a four-star hotel, this research aims to combine fuzzy clustering with tree decision techniques and random forest under R software version 4.3.3 to forecast cancellations placed close to the entry day, slightly improving the performance of individual techniques.https://www.mdpi.com/2673-4591/68/1/43hotel cancellation forecastdecision tree algorithmfuzzy C-means clusteringmachine learningrandom forest
spellingShingle Eleazar C-Sánchez
Agustín J. Sánchez-Medina
Detecting Short-Notice Cancellation in Hotels with Machine Learning
Engineering Proceedings
hotel cancellation forecast
decision tree algorithm
fuzzy C-means clustering
machine learning
random forest
title Detecting Short-Notice Cancellation in Hotels with Machine Learning
title_full Detecting Short-Notice Cancellation in Hotels with Machine Learning
title_fullStr Detecting Short-Notice Cancellation in Hotels with Machine Learning
title_full_unstemmed Detecting Short-Notice Cancellation in Hotels with Machine Learning
title_short Detecting Short-Notice Cancellation in Hotels with Machine Learning
title_sort detecting short notice cancellation in hotels with machine learning
topic hotel cancellation forecast
decision tree algorithm
fuzzy C-means clustering
machine learning
random forest
url https://www.mdpi.com/2673-4591/68/1/43
work_keys_str_mv AT eleazarcsanchez detectingshortnoticecancellationinhotelswithmachinelearning
AT agustinjsanchezmedina detectingshortnoticecancellationinhotelswithmachinelearning