This is a subjective question, bence you have to write your answer in the fext-Field given below. a. Explain the concept of the L1 and L2 regularization terms in the context of Lasso and Ridge regression, respectively. How do these regularization terms affect the model's coeficients? 85096 marks] b. Both Lasso and Ridge regression involve adding penalty terms to the linear regression cost function. How do yo the regularization hyperparameters (alpha or lambda) for each technique, and what impact does the choice of to you choose the optimal values of models' bias-variance trade-ofi? Explain. c. In terms of feature selection, how can Lasso regression assist in identifying and eliminating irrelevant or redundant predictors? Describe the process and advantages of using Lasso for feature selection