Locate 2018 Q3 data for the Loan Database and the Declined loan data.

Locate the Lending Club data dictionary for the loans that were approved and funded. Note all of the data attributes listed in the Excel files (csv) as fields,

• Which attributes do you think might predict which loans will go delinquent and which will ultimately be fully repaid?
• How could we test that?

Now consider the declined loans data set of LendingClubfor Q3 2018.

• What three items do you believe would be most useful in predicting loan acceptance or rejection?
• What additional data do you think could be solicited either internally or externally that would help you predict loan acceptance or rejection?
• If you were in a position to accept or deny a loan application, how might you look differently at this data? Would you be more lenient or stringent? After reviewing your classmate's responses, do you agree or disagree with their position? Did their rationale change your mind?

Adapted from Data Analytics for Accounting (Richardson, et al., 2018)

150-200 words!

Please use the reject under chapter 1 on the attached so you do not have to register.

Data File Links.docx