The Advertising data set is provided to you. Here is a scenario. Suppose that you are a statistical consultant hired by a client to investigate the association between advertising and sales of a particular product. The Advertising data set consists of the sales of that product in 200 different markets, along with advertising budgets for the product in each of those markets for three different media: TV, radio, and newspaper. It is not possible for our client to directly increase sales of the product. On the other hand, they can control the advertising expenditure in each of the three media. Therefore, if we determine that there is an association between advertising and sales, then we can instruct our client to adjust advertising budgets, thereby indirectly increasing sales. In other words, our goal is to develop an accurate model that can be used to predict sales on the basis of the three media budgets. In this setting, the advertising budgets are input variables or predictors while sales is an output variable or response. We will now try to predict sales using the other variables in this data set. In the Advertising data set, sales is measured in thousands of units, and TV, radio, and newspaper budgets, are measured in thousands of dollars. (i) Split the data with 80 percent of data into training and 20 percent to the test set. Then scale the X train and X test data using Standard Scalar. (ii) Fit a LASSO regression on the training data using sales as the target variable and all other variables as the predictors using alpha = 2. Are all the predictors statistically significant? Predict on the test dataset and report the RMSE and MAPE. (2 points) (iii) Write the LASSO regression equation. Predict the sales for the new data point in which TV = 137, radio = 10, newspaper = 14. (2 points) (iv) Find the optimal value of alpha for LASSO regression using grid search cross-validation using all training datasets. What is the best alpha value? (2 points) (v) Fit a LASSO regression on the training data using the best alpha chosen, with sales as the target variable and all other variables as the predictors. Predict on the test dataset and report the RMSE and MAPE. Did you see the improvement in the RMSE and MAPE scores? (2 points) (vi) Write the LASSO regression equation. Predict the sales for the new data point in which TV = 137, radio = 10, newspaper = 14. (2 points) (vii) (Bonus) Create appropriate interactive visualization for the lasso regression residuals that provides new insights to your results. (2 points)