Credit Default Risk Analysis

This project analyzes factors that may influence the probability of customer loan default using a real-world dataset from a financial institution. The analysis focuses on identifying trends and relationships across demographic and financial variables.

Project Objectives 📊

• Understand which customer attributes contribute to higher or lower loan default risk.
• Provide insights that can help credit divisions make more informed lending decisions.
• Apply data cleaning, transformation, and exploratory analysis techniques.

Key Analysis & Hypotheses Tested 🔍

• Number of Children: Are customers with more children more likely to default?
• Family Status: How does marital status influence default probability?
• Income Level: Do low-income customers default more often?
• Loan Purpose: Are loans for education or car purchases riskier?

Tools Used

• Python (pandas, matplotlib, seaborn)
• Jupyter Notebook

Files

• credit_analysis.ipynb — the main notebook containing all analysis steps
• credit_scoring_eng.csv — source data file

Notes

• Outliers and missing values were addressed using median imputation and basic filtering.
• The dataset was cleaned for inconsistencies like casing differences and invalid values (e.g., age = 0, children = -1).

Author

Nabilla Hafsah Caesaredia