Predicting hotel bookings cancellation during high volatility times: Using machine learning for hotel booking management during a pandemic

Detalhes bibliográficos
Autor(a) principal: Silvestre, Pedro Cruz
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/145588
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
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spelling Predicting hotel bookings cancellation during high volatility times: Using machine learning for hotel booking management during a pandemicData sciencehospitalitymachine learningpredictive modelingCOVID-19concept driftDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceLike many other service industries, booking cancellations highly impact hotel management decisions, negatively contributing to accurate forecasts. With accurate forecasts, hotels can make better decisions about bookings to accept and reject, what prices to make, and how many rooms to oversell, leading to higher revenue and lowering costs. Previous research showed it is possible to develop good-performing predictive models using hotel booking data. However, past research was not developed considering a pandemic where mass cancellations happen. Thus, it was not yet verified if performance is affected by those new circumstances. In a first study, using hotel booking data from four hotels, this new research assesses the machine learning classification models’ performance under the conditions imposed by the COVID-19 pandemic, achieving fair to excellent performance. With city hotel C1 achieving 0.72 AUC, 0.80 Accuracy, and 0.76 F1-Score, C2 achieving a 0.94 AUC, 0.93 Accuracy, and 0.94 F1-Score. Resort hotel R1 achieved 0.72 AUC, 0.59 Accuracy, and 0.69 F1-Score, and lastly, R2 achieved 0.70 AUC, 0.56 Accuracy, and 0.64 F1-Score. Besides the predictive performance, this study highlights the critical predictive features. The lead time was the most crucial feature of the XGBoost model for three of the four hotels. A second study implemented a sliding window technique to understand how the performance differs in different timespans. For the configuration of twenty-four months of training and nine months of test data, it was possible to achieve better results than in the previous study. Two windows were created, with the second window having nine months of training data since the pandemic's start. For the first window the AUC varied from 0.80 to 0.98 on test data. For the second window the AUC varied from 0.85 to 0.99 on test data. This research highlights the importance of machine learning in hospitality management, particularly during a crisis such as an unprecedented pandemic. Both studies show that it is possible to create machine learning classification models to predict hotel booking cancellations during a pandemic with fair to excellent results. Results of the second study emphasized the importance of having newer data in model training and constant monitoring. Artificial Intelligence models can help managers improve their results by providing superior accuracy in a timelier way.António, Nuno Miguel da ConceiçãoRUNSilvestre, Pedro Cruz2022-10-252024-10-25T00:00:00Z2022-10-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/145588TID:203098641enginfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:26:08Zoai:run.unl.pt:10362/145588Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:52:09.945831Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Predicting hotel bookings cancellation during high volatility times: Using machine learning for hotel booking management during a pandemic
title Predicting hotel bookings cancellation during high volatility times: Using machine learning for hotel booking management during a pandemic
spellingShingle Predicting hotel bookings cancellation during high volatility times: Using machine learning for hotel booking management during a pandemic
Silvestre, Pedro Cruz
Data science
hospitality
machine learning
predictive modeling
COVID-19
concept drift
title_short Predicting hotel bookings cancellation during high volatility times: Using machine learning for hotel booking management during a pandemic
title_full Predicting hotel bookings cancellation during high volatility times: Using machine learning for hotel booking management during a pandemic
title_fullStr Predicting hotel bookings cancellation during high volatility times: Using machine learning for hotel booking management during a pandemic
title_full_unstemmed Predicting hotel bookings cancellation during high volatility times: Using machine learning for hotel booking management during a pandemic
title_sort Predicting hotel bookings cancellation during high volatility times: Using machine learning for hotel booking management during a pandemic
author Silvestre, Pedro Cruz
author_facet Silvestre, Pedro Cruz
author_role author
dc.contributor.none.fl_str_mv António, Nuno Miguel da Conceição
RUN
dc.contributor.author.fl_str_mv Silvestre, Pedro Cruz
dc.subject.por.fl_str_mv Data science
hospitality
machine learning
predictive modeling
COVID-19
concept drift
topic Data science
hospitality
machine learning
predictive modeling
COVID-19
concept drift
description Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
publishDate 2022
dc.date.none.fl_str_mv 2022-10-25
2022-10-25T00:00:00Z
2024-10-25T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/145588
TID:203098641
url http://hdl.handle.net/10362/145588
identifier_str_mv TID:203098641
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
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dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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