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.
Although it may be tempting to think that with the advent of big data, predictive models will be more accurate, statistical theorems show that after a certain point, feeding more data into a predictive analytics model will not provide more accurate results. Analyzing representative portions of the available information (sampling) can help speed development time on models and allow them to be deployed more quickly.