Lending Club Case Study
Lending Club is a marketplace for personal loans that matches borrowers who are seeking a loan with investors looking to lend money and make a return.
Problem Statement
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
So, company wants to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default. The company can utilize this knownledge for its portfolio and risk assessment.
Problem Solving Methodology
- Data Understanding-
Understanding and working with data dictionary and getting good knowledge of all the columns and their domain specific uses.
- Data Cleaning-
Removing null valued columns, single uniques columns, unnecessary columns then manipulation of data such as conversion of data types, removing outliers, deriving new variables and many more.
- Univariate Analysis-
Analysing each columns and plotting the distribution of each to get more information.
- Segmented Univariate Analysis-
To get more insight of single data variables in the form of segments.
- Bivariate Analysis-
Through this we can analyse two variables and determine empirical relationship between them.
- Recommendations-
Atlast we can recommend the investor what all variables to be considered while approving the loan so that loss of the company can be reduced.
Technologies Used
- Python - version 3.x
Libraries Used
- Pandas , Numpy , Matplotlib , Seaborn