The attached file contains information about 5,000 customer data of Universal Bank, a relatively young bank. (This dataset is provided by one of the books in this class, "Data Mining for Business Analytics.") The bank is interested in growing customers to bring in more loan business. The bank encouraged the marketing department to come up with an idea for better target marketing and to determine what factors make a customer accept a personal loan. The attached dataset includes demographic variables of its customers, customer response to the personal loan campaign (Personal Loan), etc. Build a decision tree and develop ensemble models, including bagging, boosting, and random forests, and compare the outcomes. (60 pts) Predict whether the following customer could get the personal loan with four models. Then, compare and interpret the results. (40 pts) Caroline is 32 years old and has six years of experience. Her education level is 3 (she graduated from law school), and her yearly income is about $200k. She has a husband and a daughter and does not have any mortgage loans. Also, she has multiple security accounts and credit cards.