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Predictive modeling is a process that uses data mining and probability to forecast outcomes. Each model is made up of a number of predictors, which are variables that are likely to influence future results. Once data has been collected for relevant predictors, a statistical model is formulated. The model may employ a simple linear equation or it may be a complex neural network, mapped out by sophisticated software. As additional data becomes available, the statistical analysis model is validated or revised.
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Predictive modeling is often associated with meteorology and weather forecasting, but it has many applications in business. Bayesian spam filters, for example, use predictive modeling to identify the probability that a given message is spam. In fraud detection, predictive modeling is used to identify outliers in a data set that point toward fraudulent activity. And in customer relationship management (CRM), predictive modeling is used to target messaging to those customers who are most likely to make a purchase. Other applications include capacity planning, change management, disaster recovery, engineering, physical and digital security management and city planning.