Project Name

Lending Club Case Study

Table of Contents

• General Info
• Technologies Used
• Conclusions

General Information

• We will try to find out the applicants who are likely to default i.e. not pay the loan.
• this analysis is being done for a company which is the largest online loan marketplace. We will try to minimize the credit loss to this company.
• What is the business probem that your project is trying to solve? - whether to sanction the loan to the applicant or not.
• we have got a loans dataset which has multiple column, we will be focusing on these columns - loan_amt, term, int_rate, grade, emp_title, loan_status, annual_income

Conclusions

• Most of the data is of loan_status Fully paid, there are around 5000 rows with charged off loan status
• most loan amount is in the range of 5000 to 150000
• if the loan duration i.e. is more than 40 months, then the applicant is likely to 'default' or be in 'current' loan status
• if the annual income is less than 60000, then the applicant is highly likely to default
• if the interest rate is above 12, then applicant may 'default' or be in 'current' loan status

Technologies Used

• Python 3.7

Libraries used

• Pandas
• Numpy
• matplotlib
• seaborn

Contact

Created by [@Viveksingh1313] - feel free to contact me!