Loan Status Prediction with Machine Learning

This project develops a machine learning model to predict whether a loan will be fully paid or charged off, helping financial institutions assess credit risk and improve decision-making.

📊 Best Model: XGBRFClassifier

✔ Accuracy: 82.7%
✔ F1 Score: 0.89
✔ AUC-ROC: 0.64
✔ Inference Time: 32.3 ms

🔍 Key Insights

• Most Influential Factors:
  1️⃣ Credit Score
  2️⃣ Annual Income
  3️⃣ Years of Credit History
• XGBRFClassifier provided the best balance between precision and efficiency.
• Deployed via Streamlit Cloud for real-time predictions.

🚀 How to Run Locally

1️⃣ Clone the repository:

git clone https://github.com/astrxnomo/loan-status-prediction.git
cd loan-status-prediction

2️⃣ Install dependencies:

pip install -r requirements.txt

3️⃣ Run the Streamlit app:

streamlit run app.py

4️⃣ Open in browser: http://localhost:8501

📌 Technologies Used

• Python (pandas, numpy, scikit-learn, xgboost, joblib)
• Machine Learning Models: XGBRFClassifier, Random Forest, Logistic Regression, Gradient Boosting
• Deployment: Streamlit Cloud