Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future.
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For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs.
The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome.
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It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.
Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit 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.
Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, mobility, healthcare, child protection, pharmaceuticals, capacity planning and other fields.
Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting.
For example, "Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.
Predictive analytics finds application in verticals such as actuarial science, Banking and Financial Services, Insurance (BFSI), healthcare, government, retail, manufacturing, transportation & travel, it, entertainment, sports, and energy & utilities. Predictive analytics helps to manage the diversified applications across the above all verticals.
In the manufacturing industry, predictive analytics may be used for equipment maintenance management, cross-selling and up-selling, and workforce management.
This report studies Predictive Analytics in Global market, especially in North America, Europe, China, Japan, Southeast Asia and India, with production, revenue, consumption, import and export in these regions, from 2011 to 2015, and forecast to 2021.
This report focuses on top manufacturers in global market, with production, price, revenue and market share for each manufacturer, covering
Table of Content:-
Global Predictive Analytics Market Professional Survey Report 2016
1 Industry Overview of Predictive Analytics
1.1 Definition and Specifications of Predictive Analytics
1.1.1 Definition of Predictive Analytics
1.1.2 Specifications of Predictive Analytics
1.2 Classification of Predictive Analytics
1.2.1 Predictive Models
1.2.2 Descriptive Models
1.2.3 Decision Models
1.3 Applications of Predictive Analytics
1.3.1 Application 1
1.3.2 Application 2
1.3.3 Application 3
1.4 Market Segment by Regions
1.4.1 North America
1.4.4 Southeast Asia
2 Manufacturing Cost Structure Analysis of Predictive Analytics
2.1 Raw Material and Suppliers
2.2 Manufacturing Cost Structure Analysis of Predictive Analytics
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