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Questiоn 4: Regulаrized vаriаble selectiоn (12 pоints) For this question, use the trainData. 4a)(6 points) Perform Ridge regression on the dataset "trainData". Call it model4. Answer the following questions for this model: i) Use cv.glmnet() to find the lambda value that minimizes the cross-validation error using 10 fold CV and state the value of the optimal lambda. ii) Fit the model with 100 values for lambda. iii) Extract the coefficients from (ii) using the optimal lambda selected in (i), and list the coefficients that are selected. iv) Plot the coefficient path and place the optimal lambda from (i) on the plot. Analyze the plot and comment on which coefficients are shrunk to zero. 4b)(6 points) Perform group lasso using the dataset trainData. Call it model5. Create the following groups below for your model. Note: Only these 5 variables/3 groups should be used in your group lasso model. Group1: Age Group2: ExperienceYears and RemoteWorkHoursPerWeek Group3: JobSatisfaction and TechToolsUsed Answer the following questions: Find the lambda value that minimizes the cross-validation error using 10 fold CV and state the value of the optimal lambda. Fit the model with 100 values for lambda. Extract the coefficients from ii) using the optimal lambda from i), display the coefficients of the variables and comment on the magnitude and direction of the variables on the response. Plot the coefficient path and place the optimal lambda from i) on the plot and then analyze/interpret the plot