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The model which is created and selected for prediction of probability of a particular outcome is the predictive model. And the procedure of doing this is termed as Predictive Modeling.
It is used in predictive analytics and helps to build a Statistical model which tells about the future behaviors. Predictive Analytics involves data mining that includes trends and forecasting.
There are a number of predictors in a predictive model which acts as variables and affects future behavior. For example Customer's age, purchase etc. in marketing are used for prediction of how likely will be the future sale.
Firstly ,in predictive modeling data are collected . Statistical model is then formulated followed by prediction. And with the availability of additional data model is revised. A simple linear equation or a complex network is used for the model.
Applications
It is highly used in information technology. Predictive Modeling is also applied in spam filtering systems i.e, in spam filtering systems it is required for the identification of the probability of a message to be spam. There are other applications also , for example security management, customer relationship management, meteorology, change management, city planning etc.
The applications include the following:
 Collection analytics
 Clinical decision support systems
 Portfolio, product or economy level prediction
 Fraud detection
 Analytical customer relationship management (CRM)
 Direct marketing
 Customer retention
 Crosssell
Examples of predictive modeling are insurance claims, future share prices, customer ordering products etc. With the help of historical data or data collected through sampling Predictive models are developed. Some examples are Credit loans, Balanced Score Card, Credit loans.
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