Use glmnet() to fit LASSO models for a collection of values (as automatically selected by glmnet()). Plot the resulting object to display the model coefficients as a function of their L1 norm. Interpret this plot. IN RSTUDIO

Use cv.glmnet() to explore how the estimated Mean Squared Error changes as a function of the shrinkage parameter, . Plot the resulting object and interpret this plot. IN RSTUDIO

Select a value that corresponds to a model exhibiting an appropriate trade-off between performance and regularisation. What are the corresponding model coefficients for this model? IN RSTUDIO

Fit a multiple regression model using only those variables suggested by the LASSO model you described in the previous item. Briefly explain how and why the model coefficients for this multiple regression model are different to the LASSO model coefficients? IN RSTUDIO

id	x1	x2	x3	x4	x5	x6	x7	x8	x9	x10	x11	x12	x13	x14	x15	x16	x17	x18	x19	x20	x21	x22	x23
1	2	0	1	1	1	8.5	3.9	2.5	5.9	4.8	4.9	6	6.8	4.7	4.3	5	5.1	3.7	8.2	8	8.4	65.1	1
2	3	1	0	0	0	8.2	2.7	5.1	7.2	3.4	7.9	3.1	5.3	5.5	4	3.9	4.3	4.9	5.7	6.5	7.5	67.1	0
3	3	0	1	1	1	9.2	3.4	5.6	5.6	5.4	7.4	5.8	4.5	6.2	4.6	5.4	4	4.5	8.9	8.4	9	72.1	1
4	1	1	1	1	0	6.4	3.3	7	3.7	4.7	4.7	4.5	8.8	7	3.6	4.3	4.1	3	4.8	6	7.2	40.1	0
5	2	0	1	0	1	9	3.4	5.2	4.6	2.2	6	4.5	6.8	6.1	4.5	4.5	3.5	3.5	7.1	6.6	9	57.1	0