Predictive modeling is a process that uses data mining and probability to forecast outcomes. The outcomes may be in the financial world or the outcome of a disease management plan.
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.
Predictive modeling is most frequently associated with weather forecasting. Outside the world of meteorology, one of the most common uses of predictive modeling is in online advertising and marketing. Modelers use web users’ historical data, running it through algorithms to determine what kinds of products users might be interested in and what they are likely to click on.
Predictive modeling and predictive analytics are often used interchangeably. However, they are not identical. Predictive modeling uses regression model and statistics to predict the probability of outcome and it can be applied to any unknown event predictive modeling is often used in the field of machine learning, artificial intelligence (AI).
Predictive analytics consists of extracting information from data to predict trends and behavior patterns. It uses present or historical data to predict future outcomes to drive better decisions.
Predictive analytics gets more attention due to the proliferation of big data and machine learning technologies. Machine learning is an area of computer science which uses cognitive learning methods to program their systems without the need of being explicitly programmed. In other words, those machines are well known to grow better with experience.
Machine learning is related to other mathematical techniques and also with data mining which encompasses terms such as supervised and unsupervised learning.
Businesses are complex and customers aren’t one-dimensional, so the more data available when making decisions, the better those decisions will be.
Melody K. Smith
Sponsored by Access Innovations, the world leader in thesaurus, ontology, and taxonomy creation and metadata application.