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Data mining techniques
2.1Assuming that data mining techniques are to be used in the following cases, identify whether the task required is supervised or unsupervised learning. a.Supervised-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.Unsupervised-In an online bookstore, making recommendations to customers concerning additional items to buy based on the buying patterns in prior transactions. c.Supervised-Identifying a network data packet as dangerous (virus, hacker attack) based on comparison to other packets whose threat status is known.
d.Unsupervised-Identifying segments of similar customers.
e.Supervised-Predicting whether a company will go bankrupt based on comparing its financial data to those of similar bankrupt and non-bankrupt firms. f.Unsupervised-Estimating the repair time required for an aircraft based on a trouble ticket. g.Supervised-Automated sorting of mail by zip code scanning. h.Unsupervised-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.
It’s not good because when looking at models you want to see the relationship between the data if there are zero error in the data then the information you get is skewed and may not be a true reflection. 2.7A dataset has 1000 records and 50 variables with 5% of the values missing, spread randomly throughout the records and variables. An analyst decides to remove records that have missing values. About how many records would you expect would be removed? Trick question – all because none of the records would be useable.
Date: Oct 13,2021