LoanApproval-DT-Pruned A Decision Tree Model for Predicting Loan Approvals
📌 Overview This is a pruned Decision Tree model trained to predict loan approval decisions based on customer information. It is optimized for performance by reducing overfitting through cost-complexity pruning.
🏦 Dataset The model was trained on a financial dataset containing:
Applicant details: income, employment status, credit history, marital status, etc. Loan-specific details: loan amount, term, and approval status. Target variable: Approval_Status (Y for approved, N for rejected).
🛠 Model Details Algorithm: Decision Tree Classifier (pruned) Pruning Parameter: ccp_alpha = 0.0096 Evaluation Metrics: Accuracy: 0.8361 Precision: 0.8077 Recall: 1.0000 F1 Score: 0.8936
🚀 How to Use Load the Model python Copy Edit import joblib
Load the model
model = joblib.load("best_pruned_dt.pkl")
Sample prediction
sample_input = [[5000, 0, 1, 1, 200, 360, 1]] # Example feature set prediction = model.predict(sample_input)
Output: ['Y'] or ['N']
print("Loan Approval Prediction:", prediction)
⚖️ License This model is licensed under the Creative Commons Attribution 4.0 (CC-BY-4.0) license. You are free to share, modify, and use this model for any purpose, including commercial use, as long as you provide proper attribution to the original creator.
Attribution Requirements: If you use or modify this model, please credit: 📝 Author: https://ifiecas.com/ 🔗 Source: https://huggingface.co/ifiecas/LoanApproval-DT-v1.0/edit/main/README.md 📜 License Details: CC-BY-4.0
🏆 Acknowledgments This model was developed as part of (BCO6008) Predictive Analytics at Victoria University for automating loan qualification processes.
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