"Multicollinearity" a Problem or an Opportunity?

Multicollinearity (Collinearity) is not a new term especially when dealing with multiple regression models. This phenomenon of relationship in between one response variable with the set of predictor variables also include models like classification and regression trees as well as neural networks. Collinearity is infamously famous for inflating the variance of at least one estimated regression coefficient, which can cause the model to predict erroneously and in a business setup it can have an unrepairable consequence. So, the next logical question is how to identify collinearity? In this article we will only talk about the Variance Inflation Factor(VIF) identification technique which is very useful for identify high multicollinearity among the predictor variables when working with MLR (Multiple Linear Regression Models).