Machine Learning

Financial Risk Analysis and Loan Default Prediction Using Machine Learning

The Financial Risk Analysis and Loan Default Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts loan default risk using Lasso and Ridge regression models with SMOTE for class imbalance handling. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It uses regularized linear models with leak-free cross-validation and SMOTE to achieve robust credit risk assessment.

The system leverages 29 engineered features including credit score, applicant income, loan amount, debt-to-income ratio, employment type, marital status, and derived financial ratios like loan-to-income and debt-to-income ratio. It provides interactive visualizations, feature importance analysis, model comparison, and real-time loan default prediction to help financial institutions make informed lending decisions.

• Binary Loan Default Prediction
• Ridge Regression (F1: 0.5292)
• Lasso Regression (F1: 0.4679)
• SMOTE for Class Imbalance
• Leak-Free Cross-Validation
• Feature Importance Analysis
• Real-time Default Prediction
• Model Performance Comparison
• Interactive Visualizations
• Flask Web Application