Graph based approach for minimum multicollinearity highly accurate regression model explaining maximum variability

Regression is a supervised machine learning method which advances by building a equation by which we can estimate the response variable. The proper choice and number of predictors used for prediction of response variable plays an important role. Multicollinearity affects the model accuracy hence emphasis has to be made to keep it as low as possible. The coefficients of the regression equation are calculated by X^TX. In case of strong correlation amongst attributes it is difficult for software to calculate this term. In addition to all this the approach has to be pictorial or graphical in nature for better comprehensibility which is unique. This paper keeps in mind all these factors and proposes a technique for building a best (accurate, minimum multicollinearity, graphical) regression model.