A large online loan marketplace, facilitating personal loans, business loans, wants to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default.
- Background: The aim of the case study is to identify applicants who are likely to default using EDA(Exploratory Data Analysis).
- Business Problem: A large online loan marketplace, facilitating personal loans, business loans, wants to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default.
- Dataset: The data contains records of loans issued, including fields such as issue month and loan amounts. The primary analysis revolves around the total number of loans issued per month.
- In this analysis of Lending Club loan data, we aimed to identify the key factors influencing loan defaults through a comprehensive Exploratory Data Analysis (EDA).
- The univariate and segmented univariate analyses revealed that factors such as loan amount, interest rate, employment length, and DTI are critical indicators of loan defaults.
- Combining these insights in the bivariate analysis further strengthened the findings, confirming that higher-risk borrowers typically took larger, higher-interest loans and had shorter employment histories or higher financial burdens.
- Python - version 3.12.4
- Pandas - for data manipulation and analysis
- Matplotlib - for generating visualizations such as bar charts
Give credit here.
- This project was inspired by financial data analysis and aims to provide practical insights to stakeholders in the lending industry.
- Special thanks to upgrad for providing loan datasets that enabled this analysis.
Created by [@premchavan3399] - feel free to contact me!