Lending Club Defaulter EDA

This exploratory data analysis aims at exploring the loans provided by Lending Club and the hidden intricacies of people defaulting.

General Information

I 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.
The 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 utilise this knowledge for its portfolio and risk assessment.
Dataset contains the complete loan data for all loans issued through the time period 2007 t0 2011.

High ticket size loans have more defaulters
DTI ratio from 15-25 have more defaulters
Annual income which is also a factor for more defaulters
Meaning company is giving out loans to people with low income but high debt
Interest rates higher than 16% are often causing people to default
A Grade loans generally have more non defaulters which can prove to be a great income source
Where people are not specifically letting the company know if they are living on rent or mortgage those people are mostly defaulting
Verified customers are a hugh cause of defaulters they might be covering themselves well
The state of NE are where these defaulters are emerging from. Also some specific zip codes have shown signs of customers with higher chance of default
Months of 5,9 and 12 are showing spikes in number of defaulters

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