A consumer finance company that specializes in lending various types of loans to urban customers. When the company receives a loan application, the company has to make a decision for loan approval based on the applicant’s profile. Two types of risks are associated with the bank’s decision:
-If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company.
-If the applicant is not likely to repay the loan, i.e. he/she is likely to default, then approving the loan may lead to a financial loss for the company.
- To identify the risky loan applicants, and thereby cut down the amount of credit loss we are using loan.csv. This data set contains the complete loan data for all loans issued through the time period 2007 to 2011.
- The data given contains information about past loan applicants and whether they ‘defaulted’ or not. The aim is to identify patterns that indicate if a person is likely to default, which may be used for taking actions such as denying the loan, reducing the amount of the loan, lending (to risky applicants) at a higher interest rate, etc. In this case study, we will use EDA to understand how consumer attributes and loan attributes influence the tendency of default.
- Applicants with their OWN house accommodation are preferable
- Applicants choosing short loan terms is preferable
- Interest rate doesn't show a significant impact for defaulters. They are spread across all interest rates.
-python 3
- This project is based on upgrad EDA module
- [https://learn.upgrad.com/course/4701/segment/41960/245178/749010/3772565)https://learn.upgrad.com/course/4701/segment/41960/245178/749010/3772565].
Group Facilitator : Name: SreeLakshmiKanthi
Email ID: sl.kanthi@gmail.com
Team Member Detail: Name: Nivedita
Email ID: Niveditaganda@gmail.com