Lasso regression, short for "Least Absolute Shrinkage and Selection Operator," is a linear regression technique used for variable selection and regularization. It adds a penalty term to the standard linear regression objective function, which is the sum of squared residuals. This penalty term is the absolute value of the coefficients (L1 norm) multiplied by a tuning parameter lambda (λ). In this project. I demonstrated how this could be done in R.

Trying 2