a.Identify ONE (1) analytic question that seem possible/feasible b.Justify the r

Question

a.Identify ONE (1) analytic question that seem possible/feasible

b.Justify the reason(s) why the analytic question seem suitable for the dataset

c.Identify ONE (1) learning algorithm that is suitable for the analytic question

d.Justify why this algorithm is suitable

This data set includes customers who have paid off their loans, who have been past due and put into collection without paying back their loan and interests, and who have paid off only after they were put in collection. The financial product is a bullet loan that customers should pay off all of their loan debt in just one time by the end of the term, instead of an installment schedule. Of course, they could pay off earlier than their pay schedule.

Explanation / Answer

Sample Data:

The Possible use case to this problem could be finding the policy that is likely to get its loan amount returned or not using this data set.This will help the financial institution predict the best suitable policy and lending years.

Justification

Mutlivariant logistic regression algorithm could be easily used or Decision Tree Algorithm will also work because it is supervised classifiaction problem also the data is binary so both the algorithm are suitable for it.

refrences:

Logistic Regression----->https://www.analyticsvidhya.com/blog/2015/11/beginners-guide-on-logistic-regression-in-r/

Desicion Tree ---> http://mines.humanoriented.com/classes/2010/fall/csci568/portfolio_exports/lguo/decisionTree.html

Customer ID Loan Paid(Past Due) Loan Not Paid(Past Due) Installment asdf 0 1 12 year qwerty 1 0 15 year zxcv 0 1 20 year

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