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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) 
Antonio, N., De Almeida, A. & Nunes, L. (2020). A hotel's customers personal, behavioral, demographic, and geographic dataset from Lisbon, Portugal (2015–2018). Data in Brief. 33
Exportar Referência (IEEE) 
N. M. António et al.,  "A hotel's customers personal, behavioral, demographic, and geographic dataset from Lisbon, Portugal (2015–2018)", in Data in Brief, vol. 33, 2020
Exportar BibTeX 
@article{antónio2020_1713996460071,
	author = "Antonio, N. and De Almeida, A. and Nunes, L.",
	title = "A hotel's customers personal, behavioral, demographic, and geographic dataset from Lisbon, Portugal (2015–2018)",
	journal = "Data in Brief",
	year = "2020",
	volume = "33",
	number = "",
	doi = "10.1016/j.dib.2020.106583",
	url = "https://www.sciencedirect.com/journal/data-in-brief"
}
Exportar RIS 
TY  - JOUR
TI  - A hotel's customers personal, behavioral, demographic, and geographic dataset from Lisbon, Portugal (2015–2018)
T2  - Data in Brief
VL  - 33
AU  - Antonio, N.
AU  - De Almeida, A.
AU  - Nunes, L.
PY  - 2020
SN  - 2352-3409
DO  - 10.1016/j.dib.2020.106583
UR  - https://www.sciencedirect.com/journal/data-in-brief
AB  - This data article describes a hotel customer dataset with 31 variables describing a total of 83,590 instances (customers). It comprehends three full years of customer behavioral data. In addition to personal and behavioral information, the dataset also contains demographic and geographical information. This dataset contributes to reducing the lack of real-world business data that can be used for educational and research purposes. The dataset can be used in data mining, machine learning, and other analytical field problems in the scope of data science. Due to its unit of analysis, it is a dataset especially suitable for building customer segmentation models, including clustering and RFM (Recency, Frequency, and Monetary value) models, but also be used in classification and regression problems.
ER  -