Data Mining and Predictive Analytics for E-Tourism

Nuno Antonio (), Ana de Almeida () and Luis Nunes ()
Additional contact information
Nuno Antonio: Universidade Nova de Lisboa
Ana de Almeida: ISCTE-IUL
Luis Nunes: ISCTE-IUL

Chapter 22 in Handbook of e-Tourism, 2022, pp 531-555 from Springer

Abstract: Abstract 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.

Keywords: Database mining; Data visualization; Knowledge discovery; Machine learning; Predictive analytics; Predictive modeling (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-48652-5_29

Ordering information: This item can be ordered from
http://www.springer.com/9783030486525

DOI: 10.1007/978-3-030-48652-5_29

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().