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Data Preprocessing

In: Machine Learning for Practical Decision Making

Author

Listed:
  • Christo El Morr

    (York University)

  • Manar Jammal

    (York University)

  • Hossam Ali-Hassan

    (York University, Glendon Campus)

  • Walid El-Hallak

    (Ontario Health)

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).

Suggested Citation

  • Christo El Morr & Manar Jammal & Hossam Ali-Hassan & Walid El-Hallak, 2022. "Data Preprocessing," International Series in Operations Research & Management Science, in: Machine Learning for Practical Decision Making, chapter 0, pages 117-163, Springer.
    • Handle: RePEc:spr:isochp:978-3-031-16990-8_4
    DOI: 10.1007/978-3-031-16990-8_4
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