Using your knowledge of classification, regression and data exploration, answer the following questions • Produce a model that can classify whether the client subscribes to term deposit. That is, given a client’s information and campaign information, the classifier should produce a class label showing the likely subscription to term deposit. • What would be the top five attributes that best determine the subscription to term deposit? In listing the top five attributes, discuss how you determine them and supplement with appropriate Orange files if available. Your discussion should be in about 500 words. • Produce a model to predict the likely balance of a given client’s information and campaign information. • Can we predict the balance for clients who are “widowed” and “defacto”? If no, explain why. If yes, explain how you would do so. Discuss in about 500 words. Task 3 Discuss how you would ensure that the models you produced in Task 2 are reliable and accurate. Discuss this in about 500 words. Task 4 As noted in the introduction, there are missing values in the data set. Discuss what you would do with these missing values. Do you remove them, attempt to provide values to these unknowns, or attempt a combination of different techniques? Your discussion should be in about 500 words.