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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
António, N., de Almeida, A. & Nunes, L. (2017). Using data science to predict hotel booking cancellations. In  P. Vasant,  M. Kalaivanthan  (Ed.), Handbook of research on holistic optimization techniques in the hospitality, tourism, and travel industry. (pp. 141-167).: IGI Global.
Exportar Referência (IEEE) 
N. M. António et al.,  "Using data science to predict hotel booking cancellations", in Handbook of research on holistic optimization techniques in the hospitality, tourism, and travel industry,  P. Vasant,  M. Kalaivanthan , Ed., IGI Global, 2017, pp. 141-167
Exportar BibTeX 
@incollection{antónio2017_1732205136782,
	author = "António, N. and de Almeida, A. and Nunes, L.",
	title = "Using data science to predict hotel booking cancellations",
	chapter = "",
	booktitle = "Handbook of research on holistic optimization techniques in the hospitality, tourism, and travel industry",
	year = "2017",
	volume = "",
	series = "",
	edition = "",
	pages = "141-141",
	publisher = "IGI Global",
	address = "",
	url = "http://www.igi-global.com/gateway/chapter/170956"
}
Exportar RIS 
TY  - CHAP
TI  - Using data science to predict hotel booking cancellations
T2  - Handbook of research on holistic optimization techniques in the hospitality, tourism, and travel industry
AU  - António, N.
AU  - de Almeida, A.
AU  - Nunes, L.
PY  - 2017
SP  - 141-167
DO  - 10.4018/978-1-5225-1054-3.ch006
UR  - http://www.igi-global.com/gateway/chapter/170956
AB  - Booking cancellations in the hospitality industry not only generate revenue loss and affect pricing and inventory allocation decisions, but they also, in overbooking situations, have the potential to affect the hotel’s online social reputation. By employing data sets from four resort hotels and addressing this issue as a classification problem in the scope of data science, the authors demonstrate that it is possible to build models for predicting booking cancellations with accuracy results in excess of 90%. This research also demonstrates that despite what was alleged by Morales and Wang (2010), it is possible to predict with high accuracy whether a booking will be canceled. Results allow hotel managers to act on bookings with high cancellation probability and contain the associated revenue losses, produce better net demand forecasts, improve overbooking/cancellation policies, and have more assertive pricing and inventory allocation strategies. 
ER  -