Predictive Analytics – Tools, Techniques, Algorithms, Examples.
Predictive Analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical and new data sets. It is a form of advanced analytics.
Why do we use Predictive Analytics?
Predictive Analytics helps the Organizations to become highly competitive and to expand bottom line,many organizations are using Predictive analytics. It is a very helpful source in various activities such as:-
Optimizing Marketing Campaigns.
Who uses Predictive Analytics?
Almost all industries! Yes, Predictive Analytics is catching prime importance and is being widely used. Following industries are using it-
Banking and Financial services
Governments and Public sector
Oil, gas and utilities
These industries have employed teams of Data Scientists, Statisticians and other skilled data analysts, since it requires higher level of expertise with statistical methods and machine learning. These experts use various analytical tools which are either provided by software giants such as Microsoft, IBM, SAS institute etc or some open sources software such as R, Python & Scala programming language.
How do we use Predictive Analytics?
Predictive models use known results to develop a model to predict results for new/different data. Wherein, the widely used Predictive modelling Techniques are- Decision tree, regression models and neural networks. Each model is made up of a number of predictors, which are variables that are likely to influence future results. As additional data becomes available, the statistical analysis model is validated or revised.
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