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Antonio, N., de Almeida, A. & Nunes, L. (2022). Data mining and predictive analytics for E-tourism. In Zheng Xiang, Matthias Fuchs, Ulrike Gretzel, Wolfram Höpken (Ed.), Handbook of e-Tourism.: Springer.

N. M. António et al.,  "Data mining and predictive analytics for E-tourism", in Handbook of e-Tourism, Zheng Xiang, Matthias Fuchs, Ulrike Gretzel, Wolfram Höpken, Ed., Springer, 2022

@incollection{antónio2022_1734976706435,
	author = "Antonio, N. and de Almeida, A. and Nunes, L.",
	title = "Data mining and predictive analytics for E-tourism",
	chapter = "",
	booktitle = "Handbook of e-Tourism",
	year = "2022",
	volume = "",
	series = "",
	edition = "",
	publisher = "Springer",
	address = "",
	url = "https://link.springer.com/referenceworkentry/10.1007/978-3-030-05324-6_29-1#citeas"
}

TY  - CHAP
TI  - Data mining and predictive analytics for E-tourism
T2  - Handbook of e-Tourism
AU  - Antonio, N.
AU  - de Almeida, A.
AU  - Nunes, L.
PY  - 2022
DO  - 10.1007/978-3-030-05324-6_29-1
UR  - https://link.springer.com/referenceworkentry/10.1007/978-3-030-05324-6_29-1#citeas
AB  - Computers and devices, today ubiquitous in our daily life, foster the generation of vast amounts of data. Turning data into information and knowledge is the core of data mining and predictive analytics. Data mining uses machine learning, statistics, data visualization, databases, and other computer science methods to find patterns in data and extract knowledge from information. While data mining is usually associated with causal-explanatory statistical modeling, predictive analytics is associated with empirical prediction modeling, including the assessment of the quality of the prediction. This chapter intends to offer the readers, even those unfamiliar with this topic, a general overview of the key concepts and potential applications of data mining and predictive analytics and to help them to successfully apply e-tourism concepts in their research projects. As such, the chapter presents the fundamentals and common definitions of/in data mining and predictive analytics, including the types of problems to which it can be applied and the most common methods and techniques employed. The chapter also explains what is known as the life cycle of data mining and predictive analytics projects, describing the tasks that compose the most widely employed process model, both for industry and academia: the Cross-Industry Standard Process for Data Mining, CRISP-DM.
ER  -