A Machine Learning based Credit Risk Assessment System developed as part of the CodeAlpha Internship Program.
This application predicts whether a customer is a Good Risk or Bad Risk borrower using Machine Learning techniques. The system helps financial institutions evaluate loan applications and make better lending decisions.
- Credit Risk Prediction
- Loan Recommendation
- Risk Categorization
- Probability Analysis
- Real-Time Assessment
- Interactive Streamlit Dashboard
- German Credit Dataset
- 1000+ Customer Records
- 9 Input Features
- Binary Classification Problem
- Logistic Regression
- Random Forest
- XGBoost
Model comparison was performed using 5-Fold Cross Validation.
Random Forest Classifier
Accuracy: 71.3%
- Python
- Pandas
- NumPy
- Scikit-Learn
- XGBoost
- Streamlit
- Joblib
credit_scoring/
├── data/
├── models/
├── screenshots/
├── app.py
├── train.py
├── train_xgb.py
├── model_comparison.py
├── requirements.txt
└── README.md
Install dependencies:
pip install -r requirements.txt
Run Streamlit App:
streamlit run app.py
Add project screenshots inside the screenshots folder.
Vaibhav