Predictive Modeling Definition
Predictive modeling is a technique that uses a set of data to predict future outcomes. This includes using probability and data mining procedures in the forecast of outcomes. Predictive modeling as a data mining technique uses a statistical approach for its predictions, this approach entails the use of past and present data to forecast future outcomes.
Predictive modeling can be used by analysts and businesses that seek to forecast future outcomes. The processes involved in predictive modeling include the analysis of data, creation or generation of a model, and the validation of the model created.
A Little More on What is Predictive Modeling
In predictive modeling, the models generated comprise different variables that will possibly influence future outcomes. Predictive modeling is applicable in several contexts but it is predominantly used as a technique of predicting future outcomes or behaviors.
If used by a business, for example, in predicting the future of a product or service, predictive modeling involves the collection of data from the data set available for a business. Such data include customer feedback, internet review, social media comments and other resources on the internet that form a foray of data for the analysis.
To effectively predict future outcomes or behaviors, historical events in the business must be analyzed, this is in addition to present events in the business. Due to the complex nature of the data to be analyzed, businesses take advantage of predictive analytics tools for the forecast.
How Predictive Analytics Works
Predictive modeling as a tool in predictive analytics seek to answer the question; ‘What will happen in the future.’ In predictive analytics, historical and present data are important to predict future events or outcomes. There are predictive analytics tools that help in the collection of historical data and analyze them in order to have a sneak peep of what to happen in the future. In order to arrive at predictive models that will give answers to what will happen in the future, predictors are created, often based on assumptions. Neural networks and regression and the two common predictive modeling techniques used in predicting future events.
Predictive Modeling: Regression
Regression as a predictive modeling technique that evaluates the relationship or correlation between a dependent variable and an independent variable. The dependent variable otherwise called the outcome variable depends on a predictor or feature (independent variable) before a future outcome or event can be forecasted. Several types of regression exist but the most common type of regression often used in statistics is the linear regression. The linear predictive model can be used to predict a future outcome regardless of the context in which it is used.
Predictive Modeling: Neural Networks
Neural networks are computing systems commonly used in the artificial intelligence field. Neural networks form a major predictive model technique used in forecasting a future outcome or variable. As against the linear predictive model that deals with linear relationships between a dependent variable and an independent variable, neural networks are applicable to variables that have non-linear relationships or uncorrelated variables.
Neural networks use a string of intertwined nodes that represent AI and this technique in predictive modeling seeks to solve complex computation and analysis problems that are beyond human comprehension or too cumbersome for human analysts to handle. Neural networks use machine modes to predict outcomes that are unpredictable by human brains.
Other Types of Predictive Modeling
Aside from the regression and linear network predictive modeling techniques listed above, there are other predictive modeling techniques widely used by human analysts. In statistical and financial analysis, ordinary least squares, decision trees, logistic regression, multivariate adaptive regression splines (MARS), Bayesian analysis, and time-series data mining are commonly used. Any of these methods can be used by large companies that have a sea of data at their disposal. These techniques help in understanding or predicting future events or outcomes in a company, both the positive and negative ones.