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Predictive Analytics

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Predictive analytics

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Predictive analytics refers to the practice of using a class of analytical techniques that involve data-driven modeling, mining, and learning over historical data to make predictions about missing, incomplete, or future data values, events, or patterns.

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Predictive analytics combines historical data with predictive models to produce additional information not readily available within the data.

Predictive analytics is often cited as a major analytics category along with descriptive analytics, which focuses on the analysis of historical data for postmortem insight, and prescriptive analytics that uses predicted data to help with antemortem decision making.

Predictive models that underlie predictive analytics are diverse yet they all commonly describe the relationships present in the data. The most popular models are based on regression (e.g., linear and logistic) and machine learning techniques (e.g., support vector machines and k-nearest...

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  1. Mert Akdere, Ugur Çetintemel, Matteo Riondato, Eli Upfal, Stanley B. Zdonik. The case for predictive database systems: opportunities and challenges. CIDR 2011: 167–74.

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Correspondence to Ugur Cetintemel .

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© 2016 Springer Science+Business Media LLC

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Cetintemel, U. (2016). Predictive Analytics. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_80669-2

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  • DOI: https://doi.org/10.1007/978-1-4899-7993-3_80669-2

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  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4899-7993-3

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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