d) Consider a company in Nairobi that decides whether to employ a fresh graduate based on the values of the following attributes: Degree (the student's degree classification), Univ (the university the student attended), Letter (the strength of the recommendation letters), and Expr (whether the student has any prior experience). To simplify the problem, assume that the possible Degree values are First, Second and Pass; possible Univ are KU, UON and MOI; possible letter values are good and bad; and finally Expr values are yes and no. Suppose that, you are hired by the company to develop a system that will help the company to decide whom to employ, and are given the following examples from the company's hiring record: Example 1234567892 Degree Univ First Attributes Letter Expr Hire UON Good Yes Y First UON Good No Y First ΜΟΙ Bad No Y Second UON Good Yes Y Second ΜΟΙ Good No Y Second KU Good Yes Y Second KU Good No Second UON Good No Pass ΜΟΙ Bad Yes 10 Pass UON Bad No 11 Pass KU Good No Pass KU Good No ZZZ N Suppose that you have decided to use the decision tree learning algorithm with the information gain computation for selecting attributes to induce a decision tree. What is the root node for the decision tree that you can obtain from this data. Please show all the computations involved.