Lending Club is the largest online loan marketplace, facilitating personal loans, business loans, and financing of medical procedures. Borrowers can easily access lower interest rate loans through a fast online Interface.
Use Exploratory Data Analysis to understand how consumer attributes and loan attributes influence the tendency of default.
Identify Risky loan applicants to reduce credit loss using EDA
Understand the driving factors behind loan default, i.e. the variables which are strong indicators of default. The company can utilise this knowledge for its portfolio and risk assessment.
Loan Dataset comprises of Lending club loan data with various numerical and categorical features for all loans issued through the time period 2007 to 2011.
1 Importing all the neccessary libraries 2 Importing data from the lending club loan dataset csv file 3 Data Cleaning 4 Exploratory Data Analysis (Univariate, Bivariate) 5 Major Observations for Loan Default/charged off 6 Recommendations
- Stop – approving loans where Loan amount/income is higher
- Reduce – number of approvals where purpose is Debt Consolidation and small business or increase interest rates
- Stop – approving loans approving higher amount loans for applicants with higher credit revolving balance (Risk)
- Reduce-approving loans to people with employment experience greater than 10 years
- Reduce-approving loans to US government based organizations
- Stop- approving loans to people with higher number of inquires (low credit score) for higher loan amounts
- Numpy
- Pandas
- Matplotlib
- Seaborn
Created by [@vikkicoder] - feel free to contact me!