2.1 Assuming that data mining techniques are to be used in the following cases, identify whether the task required is supervised or unsupervised learning. a. Deciding whether to issue a loan to an applicant based on demographic and financial data (with reference to a database of similar data on prior customers). b. In an online bookstore, making recommendations to customers concerning addi- tional items to buy based on the buying patterns in prior transactions. c. Identifying a network data packet as dangerous (virus, hacker attack) based on com- parison to other packets whose threat status is known. d. Identifying segments of similar customers. e. Predicting whether a company will go bankrupt based on comparing its financial data to those of similar bankrupt and nonbankrupt firms. f. Estimating the repair time required for an aircraft based on a trouble ticket. g. Automated sorting of mail by zip code scanning. h. Printing of custom discount coupons at the conclusion of a grocery store checkout based on what you just bought and what others have bought previously.

There are supervised and unsupervised machine learning models. In a supervised learning model, the algorithm evaluates a labeled dataset by comparing it with another dataset called the training data. The purpose is to evaluate its accuracy on the training data. On the other hand, an unsupervised model uses the unlabeled dataset and tries to make sense of it by extracting non-existing features and patterns without the training dataset.