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Diabetes Risk Prediction

AI-Powered Machine Learning Model using XGBoost

🐍 Python ML 📊 XGBoost 🔬 Feature Engineering ✅ Cross-Validated

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Advanced ML

XGBoost classifier with optimized hyperparameters

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High Accuracy

0.8545 ROC AUC score with cross-validation

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Feature Engineering

Polynomial interactions for non-linear patterns

📖 View on GitHub 📓 View Notebook

⚠️ Note: To build the full R Markdown documentation website, you need R installed locally.

Run:


                rmarkdown::render_site()

For now, visit the GitHub repository for complete documentation and the Jupyter notebook with the ML implementation.

This project demonstrates state-of-the-art diabetes risk prediction using gradient boosting.
Model: XGBoost | Features: 8 clinical inputs | Performance: Excellent