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Predictive analytics is a set of business intelligence (BI) technologies that uncovers relationships and patterns within large volumes of data that can be used to predict behavior and events. Unlike other BI technologies, predictive analytics is forward-looking, using past events to anticipate the future. [ 3 ]
For example, predictive models are often used to detect crimes and identify suspects, after the crime has taken place. [2] In many cases, the model is chosen on the basis of detection theory to try to guess the probability of an outcome given a set amount of input data, for example given an email determining how likely that it is spam.
Big data analytics has been used in healthcare in providing personalized medicine and prescriptive analytics, clinical risk intervention and predictive analytics, waste and care variability reduction, automated external and internal reporting of patient data, standardized medical terms and patient registries.
It is the most widely-used analytics model. [2] In 2015, IBM released a new methodology called Analytics Solutions Unified Method for Data Mining/Predictive Analytics [3] [4] (also known as ASUM-DM), which refines and extends CRISP-DM.
Analytics may variously involve data mining, process mining, statistical analysis, predictive analytics, predictive modeling, business process modeling, data lineage, complex event processing, and prescriptive analytics. For example within banking industry, academic research has explored potential for BI based analytics in credit evaluation ...
The materials in the Data Science and Predictive Analytics (DSPA) textbook have been peer-reviewed in the Journal of the American Statistical Association, [5] International Statistical Institute’s ISI Review Journal, [3] and the Journal of the American Library Association. [4] Many scholarly publications reference the DSPA textbook. [6] [7]
With data usage on the rise, it’s no surprise that someone like Cynthia Frelund, a predictive analytics expert, has carved out a niche in the sports media world.
Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets. Examples include fuzzy logic, fuzzy and rough sets for handling uncertainty, [3] neural networks for approximating functions, [4] global optimization and evolutionary computing, [5] statistical learning theory, [6] and Bayesian methods. [7]
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