🤖 Loan Default Prediction – Automating Bank Credit Processes Using AI

This project applies AI and machine learning to predict whether a customer is likely to default on a loan, enabling banks to make informed and automated credit decisions.

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📊 Problem Statement

Banks often struggle to accurately assess credit risk and loan default probability. This project automates that process by using classification models to predict default based on applicant data.

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🧠 Key Features

• Predicts loan defaults using historical banking data
• Compares multiple ML algorithms (Logistic Regression, Random Forest, XGBoost, etc.)
• Evaluates models via ROC-AUC, F1 score, Precision, and Recall
• Provides explainability with SHAP (SHapley Additive exPlanations)
• Visualization of feature importance and model decisions
• End-to-end pipeline from data preprocessing to model interpretation

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🧰 Tech Stack

• Python
• scikit-learn
• XGBoost
• SHAP
• pandas, matplotlib, seaborn

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🛠️ Setup Instructions

1. Clone the Repository

git clone https://github.com/aimaster-dev/default_loan_prediction.git
cd default_loan_prediction

2. Install Dependencies

pip install -r requirements.txt

3. Run the Notebook

Open loan_default_prediction.ipynb in Jupyter or run it using:

jupyter notebook loan_default_prediction.ipynb

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📈 Model Evaluation

• Best Model: XGBoost
• ROC-AUC: High discriminative power
• SHAP Values: Used to interpret individual predictions and global feature impact

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📌 Highlights

• Automated model selection and tuning
• Transparent credit risk scoring using SHAP
• Business-focused evaluation for banking applications

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📎 Resources

• 📄 Project Report (PDF)

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📜 License

MIT License. See LICENSE for details.

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🙌 Acknowledgments

Thanks to aimaster-dev for sharing this impactful project in AI-driven finance.