Lending Club Case Study-Exploratory Data Analysis(Upgrad)
#Problem Statement This firm facilitates personal loans, corporate loans, and finance for medical operations. It is the largest online lending marketplace. Through a quick internet interface, borrowers can readily get loans with cheaper interest rates. Like most other lenders, the biggest cause of loss is giving loans to "risky" borrowers (called credit loss). When a borrower defaults on a loan or flees with the money owed, the lender suffers a credit loss. In other words, defaulting borrowers result in the biggest loss for lenders. The "defaulters" in this instance are the consumers who have been marked as "charged-off."
Target By identifying the contributing variables to loan default, EDA approaches can help reduce credit losses.
End Goal Reduce the risk of losing money while lending to customers by using data.
Steps-EDA of Lending Club Case Study 1.Data Understanding 2.Data Cleaning 3.Data Analysis and Data Visulizations 4.Conclusion and Recommendations
- Python,Pandas,Numpy,Matplotlib,Seaborn,Plotly
- Explore EDA Recommedations which through we reduce below risk -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