ScoreCardModel

ScoreCardModel is a professional and modern toolset for scorecard modeling, fully compatible with scikit-learn. It is designed for credit risk analysts and data scientists who need to build transparent, regulator-friendly scoring models with ease.

Key Features

• 🛠 Scikit-Learn Compatible: Use BinningTransformer, WOETransformer, and ScoreCardTransformer directly in your pipelines.
• 📊 Rich Analytics: 18+ plot types (KS, ROC, CAP, Lift, Calibration, PSI, etc.) for comprehensive model evaluation.
• 📝 Automated Reporting: Generate professional Markdown or Excel reports with one function call.
• 🔄 5 WOE Methods: Choose between standard, adjusted, empirical logit, signed, and weighted Weight of Evidence.
• 🎮 Interactive Dashboard: A Jupyter-based what-if widget for real-time scorecard testing.
• 🏢 Industry Standard: Built-in support for PDO (Points to Double Odds) and Base-Odds scaling.

Documentation Sections

🚀 Getting Started

• Installation — Install via pip or uv.
• Quickstart — Build your first scorecard in minutes.

📖 User Guides

• WOE In-Depth Guide — Deep dive into WOE methods, diagnostics, and IV interpretation.
• Best Practices — Guidelines for developing robust credit risk scorecards.

🛠 API Reference

• Full API Reference — Detailed documentation for all modules and classes.

🧪 Examples

• Full Examples — End-to-end scorecard development with real-world datasets.
• Interactive Notebook — Live what-if widget in action.

License

This project is licensed under the MIT License - see the LICENSE file for details.