You work for a consumer finance company which specialises 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:
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If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company
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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
- General Information
- Technologies Used
- Conclusions
- Technology used
- Acknowledgements
When a person applies for a loan, there are two types of decisions that could be taken by the company:
Loan accepted: If the company approves the loan, there are 3 possible scenarios described below:
Fully paid: Applicant has fully paid the loan (the principal and the interest rate)
Current: Applicant is in the process of paying the instalments, i.e. the tenure of the loan is not yet completed. These candidates are not labelled as 'defaulted'.
Charged-off: Applicant has not paid the instalments in due time for a long period of time, i.e. he/she has defaulted on the loan
DATA_DICTIONARY is also available.
Data cleaning used for removing few columns and standerlize data
- Mostly people take upto 30k Loan
- People are taking more loan to pay other debt
- Higher the term and loan amount, higher Charged off's
- Intrest rate is also increased when Loan amount increase
- Loan amount and installment has high correlation
- Annual Income and int_rate has negative correlation
- NUMPY , PANDA
- PYTHON
- MATPLOTLIB , SEABORN
Give credit here.
- This project was completed by learnings of python visualization
- Used EDA techniques which was covered so far
- This project was based on lending club
- Univariate study and project code written by karan
- Further approaches and bivaraiate written by Surendra babu kunka
Created by KARAN Singh BISHT and Surendra babu kunka batch : ML C44