PROBLEMS 2.1 2.2 2.3 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. Describe the difference in roles assumed by the validation partition and the test parti- tion. Consider the sample from a database of credit applicants in Table 2.15. Comment on the likelihood that it was sampled randomly, and whether it is likely to be a useful sample. TABLE 2.15 SAMPLE FROM A DATABASE OF CREDIT APPLICATIONS OBS CHECK DURATION HISTORY NEW USED FURNITURE RADIO EDUC RETRAIN AMOUNT SAVE RESPONSE ACCT 1 0 6 8 1 36 16 0 24 24 1 12 32 0 24 40 1 9 48 0 6 56 3 64 1 72 3 80 1 88 1 96 1 104 1 112 2 6%70615 48 30 36 54 9 CAR CAR TV ACCT ODO O O O O O O O O O O O 422422210422042 00 0 1 00 0 1 ODOO 0 1 0 0 1169 4 1 0 0 0 0 6948 0 1 0 1 0 0 1282 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 LOOOOOOoo ooo 0 0 0 0 1804 1 1 1 0 0 0 4020 ooooOOOO 1 0 0 0 0 1 0 0 1 0 D100000 0 OOOOOOOOL 0 0 458 0 0 1352 0 0 783 0024 1 1 1 1 0 1 14421 0 0 0 0 730 4 1 0 0 3832 0 1 1 0 12612 1 0 0 1 15945 0 0 0 0 1919 0 1 1 0 392 0 1