1. A method of predicting a future event associated with a business based on historical data associated with a model of past events of the business, comprising: performing, by a processor-based system for predicting the future event, a transformation of a plurality of modeling variables to obtain a linear relationship of each of the plurality of modeling variables in relation to the dependent variable, wherein the plurality of modeling variables are associated with the model, and wherein a dependent variable is associated with the model and is dependent on the plurality of modeling variables, and wherein the historical data is associated with the model; selecting, by the processor-based system, a subset of the plurality of transformed modeling variables, wherein the selecting comprises applying a selecting rule based on a log-likelihood difference that comprises determining a difference between a first model-fit statistic derived by utilizing an intercept model and a second model-fit statistic derived by utilizing an intercept-plus-covariate model; determining, by the processor-based system, a set of prediction variables; and generating, by the processor-based system, a predictive model using the set of prediction variables, wherein the predictive model predicts the future event.
7. The method of claim 1 , wherein selecting the subset of transformed modeling variables further comprises: clustering the transformed modeling variables for creating variable clusters; and selecting the set of variables from the variable clusters based on the selection rule.