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.
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...
- 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.Google Scholar
- 2.Amol Ghoting, Rajasekar Krishnamurthy, Edwin P. D. Pednault, Berthold Reinwald, Vikas Sindhwani, Shirish Tatikonda, Yuanyuan Tian, Shivakumar Vaithyanathan. SystemML: declarative machine learning on MapReduce. ICDE 2011:231–42.Google Scholar
- 3.Hellerstein JM, Ré C, Schoppmann F, Wang DZ, Fratkin E, Gorajek A, Ng KS, Welton C, Feng X, Li K, Kumar A. The MADlib analytics library or MAD skills, the SQL. PVLDB. 2012;5(12):1700–11.Google Scholar
- 4.Tim Kraska, Ameet Talwalkar, John C. Duchi, Rean Griffith, Michael J. Franklin, Michael I. Jordan. MLbase: a distributed machine-learning system. CIDR 2013.Google Scholar
- 5.Xixuan Feng, Arun Kumar, Benjamin Recht, Christopher Ré. Towards a unified architecture for in-RDBMS analytics. SIGMOD 2012:325–36.Google Scholar