In regression the linear model, Y = β0 + β1X1 + · · · + βpXp + e is used to explain the response Y using the variables X1, X2…Xn and mostly we solve this using the Least Squares Method. In this paper we have used another method of variable selection and linear regularization using the GLMNET package which is more advanced and is optimal when there are many predictors involved and it also performs the function of plotting and prediction. In GLMNET, there are the lasso and elastic-net functions which performs this linear model fitting. We took the data for shares of Apple Inc. and modeled it and tried finding what the best determinants of the closing shares price at the end of the day.