Q1. (20 p) Consider the following example dataset with three attributes as presented in the table on the right: Savings (low, medium, high), Assets (low, medium, high), Income (numerical value), and a binary class label Credit risk (good, bad). Construct a decision tree heuristically (as we did in class) to decide on the Credit Risk of an applicant. Q2. (10 p) Define each of the following in one sentence: - KDD Customer Savings Assets Income ($1000s) Credit risk 1 282 Medium High 75 Good 2 Low Low 50 Bad 3 High Medium 25 Bad 4 Medium Medium 50 Good 5 Low Medium 100 Good High High 25 Good 7 Low Low 25 Bad 8 Medium Medium 75 Good 56 - - DM Centrality - Eigenvector Centrality PageRank