In each of the following cases, identify whether the task required is supervised or unsupervised learning,...

In each of the following cases, identify whether the task required is supervised or unsupervised learning, and then identify the appropriate technique—i.e., prediction, classification, affinity or clustering analysis—that you would use. Assume that an appropriate dataset is available for your algorithm to learn from.
a. Deciding whether to issue a loan to an applicant based on demographic and financial data using a database of similar data on prior customers.
b. In an online bookstore, making recommendations to customers concerning additional items to buy based on the buying patterns in prior transactions.
c. Identifying a network data packet as dangerous (e.g., virus, hacker attack) based on comparison 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. Printing custom discount coupons at the conclusion of a grocery store checkout based on what you just bought and what others have bought previously.

Solutions

Expert Solution

a. Supervised (Classification)
This problem needs the algorithm to learn about the pattern of the applicants from the patterns of the prior applicants and then classify if they are to be given loan or not.

b. Unsupervised (Clustering)
The category of books need to be divided into different clusters, and then the suggestions need to be provided.

c. Supervised (Classification)
The data packets need to be checked with the previous data packets which have been classified into dangerous and safe categories.

d. Unsupervised (Affinity)
No prior data is given and the number of clusters is not known so it needs to find the affinity of customers to different segments.

e. Supervised (Classification)
The company information needs to be matched with those of the companies whose records are present and classified as bankrupt or not.

f. Supervised (Prediction)
The algorithm needs to use regression to predict the amount of time needed for resolution based on data from previous tickets.

g. Unsupervised (Clustering)
The category of products need to be clustered and the discount coupon is to be given based on that.

venereology answered 1 year ago

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