Lending Club Case Study (EDA Related Assignments)

The objective of the case study is to examine data patterns within an online loan market platform, which offers personal, business, and medical loans. The primary goal is to identify predictive indicators that suggest whether an individual is likely to default on a loan. By leveraging historical data analysis, the aim is to make informed decisions regarding loan approvals and mitigate the risk of defaults. Potential actions include denying loans to high-risk applicants, adjusting loan amounts, or offering loans to risky applicants with higher interest rates. The ultimate aim is to optimize lending practices and enhance financial outcomes through data-driven decision-making.

Table of Contents

• General Info
• Data Used
• Conclusions
• Technologies Used
• Contact

General Information

• By using Exploratory data analysis(EDA) to understand how data is used to minimise the risk of losing money while lending to customers.
• When the company receives a loan application, the company has to make a decision for loan approval based on the applicant’s profile. a. If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company b. 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

Data Used

Files used in this Case Study are provided from Upgrad team

• loan.csv --> this file contains the complete loan data for all loans issued through the time period 2007 t0 2011.
• Data_Dictionary.xlsx --> data dictionary file has description of all columns i.e. meaning of these variables mentioned in loan.csv

Conclusions

• Charge-Off Rates : The analysis reveals that approximately one-sixth of the total dataset members have experienced charge-offs.
• Housing Status : It was observed that a significant portion of these individuals either rented or had a mortgage.
• Verification Status : The majority of the charged-off individuals were found to have unverified verification statuses.
• Non-Compliance Issues : Numerous instances of non-compliance were identified in the loan applicant verification process, indicating areas for improvement in the lending procedure.

Technologies Used

• Python Library - version 3.0
• Jupyter Notebook - <Prince.ipynb>
• CSV file
• Excel file

Contact

Created by [https://www.linkedin.com/in/prince-43314a14a/] [https://www.linkedin.com/in/yashvi-vora-427871179]- feel free to contact us!

leanCaseStudyProject