Nkaduadjei/Loan-Default-Prediction-System
📊 Predict loan defaults reliably using a hybrid ensemble of machine learning models for enhanced accuracy and real-time insights in credit risk assessment.
What's novel
📊 Predict loan defaults reliably using a hybrid ensemble of machine learning models for enhanced accuracy and real-time insights in credit risk assessment.
Code Analysis
0 files read · 3 roundsA standard Python script that loads a CSV file, trains basic XGBoost/LightGBM models using scikit-learn pipelines, and outputs predictions with simple Matplotlib visualizations via a rudimentary Flask web interface.
Strengths
The project successfully implements a functional end-to-end pipeline for loan default prediction using well-established libraries (XGBoost, LightGBM, Pandas). The separation of concerns between the web app (`app.py`), model logic (`hybrid_ensemble_model.py`), and analysis (`comparative_analysis.py`) is logical.
Weaknesses
The project lacks any unit tests, has minimal error handling for data loading or model training failures, and makes misleading claims about 'advanced' algorithms and 'hybrid' ensembles that are likely just standard wrappers. The README's promise of an easy-to-install desktop app (.exe/.dmg) is not reflected in the codebase.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
6
Contributors
2
Files
24
Active weeks
2
Repository
Language
Python
Stars
1
Forks
0
License
MIT