/MACHINE LEARNING
Loan Default Prediction
Preprocessing with imbalance handling (SMOTE), classifiers (LightGBM/SVM), and metrics reporting for lender recommendations.
- Tools & technologies
- Finance, LightGBM, SMOTE, SVM
- Data
- Source details to be added
- Explore
- View GitHub
A look at the project
A financial risk assessment project focused on predicting loan defaults to support lending decisions.
Problem Statement:
- Predict loan default probability
- Handle imbalanced dataset challenges
- Provide actionable insights for lenders
Technical Approach:
- Advanced preprocessing techniques
- SMOTE for imbalance handling
- Multiple classifier comparison
- Comprehensive metrics reporting
Models Implemented:
- LightGBM for gradient boosting
- Support Vector Machine (SVM)
- Ensemble methods
- Feature importance analysis
Key Features:
- Imbalanced data handling with SMOTE
- Advanced feature engineering
- Model comparison and selection
- Risk assessment metrics
Business Value:
- Intended to support default-risk analysis
- Improved lending decisions
- Risk-based pricing strategies
- Regulatory compliance support
