In-depth exploratory data analysis (EDA) on a credit risk dataset to identify key patterns, trends, and potential risk factors affecting loan approvals and defaults. Credit Risk Analysis using Exploratory Data Analysis (EDA) Overview This project focuses on analyzing credit risk using Exploratory Data Analysis (EDA) techniques. The goal is to uncover key patterns, trends, and risk factors that influence loan approvals and defaults. By leveraging Python and data visualization tools, we gain insights into customer creditworthiness and financial stability.
Objective Identify factors affecting loan defaults and approvals. Analyze borrower attributes such as income, credit score, loan amount, and repayment history. Visualize trends and correlations using statistical methods. Provide data-driven insights for credit risk management. Dataset The dataset consists of historical credit transactions, including borrower information, loan details, and default status. It includes features such as:
Loan Amount Annual Income Credit Score Employment Status Debt-to-Income Ratio Loan Status (Default/No Default) Technologies Used Programming Language: Python Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn Key Steps in Analysis Data Cleaning & Preprocessing
Handling missing values and outliers Feature engineering for better insights Exploratory Data Analysis (EDA)
Univariate, bivariate, and multivariate analysis Distribution of loan amounts and credit scores Correlation analysis between financial factors and loan defaults Visualization & Insights
Heatmaps, histograms, box plots, and bar charts Trend analysis of loan approvals and defaults Results & Findings High debt-to-income ratio increases the risk of default. Borrowers with low credit scores have a higher probability of loan rejection. Loan amount and income play a crucial role in credit risk assessment.
Conclusion This project provides a detailed understanding of credit risk through data-driven analysis. The findings help financial institutions improve their risk assessment strategies and make informed lending decisions.