Lasso regression performs both variable regression and regularization aiming to fight overfitting and to some extent, dealing with multicollinearity of repressors. The method requires me to choose the parameter ƛ. In order to avoid overfitting, the data was split into we split the data set into 60% training set, 20% test set, and 20% validation set to obtain the best ƛ value with corresponding largest out of sample r-squared.