Credit risk analysis is an essential component of lending decisions in the banking industry. Understanding the factors that contribute to credit risk is crucial for managing loan portfolios and ensuring the financial health of lending institutions. The objective of this project is to analyze the data and identify key factors that contribute to credit risk using advanced statistical methods and visualizations.
The two datasets used in this project are:
"Current Application" dataset: Contains information about customers' loan applications, including credit scores, income, loan amount, and whether the loan was approved or not.
"Previous Application" dataset: Contains information about customers' previous loan applications, including the loan amount, loan type, and status.
To run the code, you need to install the following dependencies:
Python 3.x Jupyter Notebook Pandas NumPy Seaborn Matplotlib
The analysis revealed several key factors that contribute to credit risk in lending, including low credit score, high debt-to-income ratio, and low income. The project also identified interesting trends in the data, such as the relationship between loan purpose and credit risk.
This project provides an overview of credit risk analysis using advanced statistical methods and visualizations. By identifying key credit risk factors, the project helps lending institutions make better-informed lending decisions and manage their loan portfolios effectively.