Predictive analytics: Difference between revisions

Revision as of 03:35, 5 November 2024

Revision as of 03:35, 5 November 2024

Line 2:

{{More citations needed|date=June 2011}}

'''Predictive analytics''' is a form of [[business analytics|businesssssss analytics]] applying [[machine learning]] to generate a [[predictive model]] for certain business applications. As such, it encompasses a variety of statistical techniques from predictive modeling and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.<ref name=":52">{{Cite web |title=To predict or not to Predict |url=https://mccoy-partners.com/updates/to-predict-or-not-to-predict |access-date=2022-05-05 |website=mccoy-partners.com}}</ref> It represents a major subset of machine learning applications; in some contexts, it is synonymous with machine learning.<ref name="Siegel 2013">{{Cite book |last=Siegel |first=Eric |title=Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (1st ed.) |publisher=[[Wiley (publisher)|Wiley]] |year=2013 |isbn=978-1-1183-5685-2 |language=English}}</ref>

In business, predictive models exploit [[Pattern detection|patterns]] found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding [[decision-making]] for candidate transactions.<ref>{{Cite book |last=Coker |first=Frank |title=Pulse: Understanding the Vital Signs of Your Business (1st ed.) |___location=Bellevue, WA |publisher=Ambient Light Publishing |year=2014 |isbn=978-0-9893086-0-1 |pages=30, 39, 42, more}}</ref>