Data Preprocessing

Christo El Morr, Manar Jammal, Hossam Ali-Hassan and Walid El-Hallak ()
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Christo El Morr: York University
Manar Jammal: York University
Hossam Ali-Hassan: York University, Glendon Campus
Walid El-Hallak: Ontario Health

Chapter Chapter 4 in Machine Learning for Practical Decision Making, 2022, pp 117-163 from Springer

Abstract: Abstract Preprocessing is the practice of cleaning, altering, and reorganizing raw data prior to processing and analysis, which is also known as data preparation [1]. It is an important step before processing and usually entails reformatting, adjusting, and integrating datasets to improve the information contained within them. Even though data preprocessing can be an onerous task, it is necessary as a precondition for putting data into context and reducing the possibility of bias [2]. An Aberdeen Group study states that data preprocessing refers to any activity taken in order to improve the quality, usability, accessibility, and portability of data [3]. In a poll published in Forbes, data scientists reported that they spend 60% of their time on data preprocessing (Fig. 4.1).

Date: 2022
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DOI: 10.1007/978-3-031-16990-8_4

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