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Chain together data processing, model training, and deployment with minimal code**\n\n[![PyPI Version](https://img.shields.io/pypi/v/mlfcrafter?color=blue\u0026logo=pypi\u0026logoColor=white)](https://pypi.org/project/mlfcrafter/)\n[![Python Support](https://img.shields.io/pypi/pyversions/mlfcrafter?logo=python\u0026logoColor=white)](https://pypi.org/project/mlfcrafter/)\n[![Tests](https://github.com/brkcvlk/mlfcrafter/workflows/🧪%20Tests%20\u0026%20Code%20Quality/badge.svg)](https://github.com/brkcvlk/mlfcrafter/actions)\n[![Documentation](https://github.com/brkcvlk/mlfcrafter/workflows/📚%20Deploy%20Documentation/badge.svg)](https://brkcvlk.github.io/mlfcrafter/)\n[![License](https://img.shields.io/github/license/brkcvlk/mlfcrafter?color=green)](LICENSE)\n[![PyPI Downloads](https://static.pepy.tech/badge/mlfcrafter)](https://pepy.tech/projects/mlfcrafter)\n---\n\n## ⭐ **If you find MLFCrafter useful, please consider starring this repository!**\n\n\u003ca href=\"https://github.com/brkcvlk/mlfcrafter/stargazers\"\u003e\n  \u003cimg src=\"https://img.shields.io/github/stars/brkcvlk/mlfcrafter?style=social\" alt=\"GitHub stars\"\u003e\n\u003c/a\u003e\n\nYour support helps us continue developing and improving MLFCrafter for the ML community.\n\n---\n\n## What is MLFCrafter?\n\nMLFCrafter is a Python Tool that simplifies machine learning pipeline creation through chainable \"crafter\" components. Build, train, and deploy ML models with minimal code and maximum flexibility.\n\n### Key Features\n\n- **🔗 Chainable Architecture** - Connect multiple processing steps seamlessly\n- **📊 Smart Data Handling** - Automatic data ingestion from CSV, Excel, JSON\n- **🧹 Intelligent Cleaning** - Multiple strategies for missing value handling  \n- **📏 Flexible Scaling** - MinMax, Standard, and Robust scaling options\n- **🤖 Multiple Models** - Random Forest, XGBoost, Logistic Regression support\n- **📈 Comprehensive Metrics** - Accuracy, Precision, Recall, F1-Score\n- **💾 Easy Deployment** - One-click model saving with metadata\n- **🔄 Context-Based** - Seamless data flow between pipeline steps\n\n\n## Why MLFCrafter?\n\nWriting the same ML boilerplate again and again is exhausting — especially when juggling multiple datasets or experimenting with different models. MLFCrafter was created to solve exactly that.\n\nHere’s why MLFCrafter might be the right tool for you:\n\n✅ **Automation without Black Box**: You automate repetitive steps, but still keep visibility and control over each stage.\n\n✅ **Modular by Design**: You can run only the steps you need. Don't want automatic data cleaning ? Just skip `CleanerCrafter` and plug in your own function.\n\n✅ **Readable \u0026 Reusable**: The API is simple, clean, and built for easy experimentation and reproducibility.\n\n✅ **Scikit-learn Compatible**: Use your favorite tools and estimators within the pipeline.\n\n✅ **Open for Extension**: You can build your own custom crafters if needed.\n\n✅ **Easy to Learn**: MLFCrafter’s intuitive API and clear component structure make it approachable even for users with basic machine learning knowledge. You don’t need to dive deep into complex frameworks to start building.\n\n---\n\n## Documentation\n\n- Complete documentation is available -\u003e [MLFCrafter Docs](https://brkcvlk.github.io/MLFCrafter/)\n- Create your first Pipeline -\u003e [Your First Pipeline](https://brkcvlk.github.io/MLFCrafter/getting-started/first-pipeline/)\n- Start learning How Crafters work -\u003e [Crafters](https://brkcvlk.github.io/MLFCrafter/api/crafters/data-ingest-crafter/)\n- Do you want to see example usage, Check -\u003e [Example](https://brkcvlk.github.io/MLFCrafter/examples/basic-usage/)\n## Quick Start\n\n### Installation\n\n```bash\npip install mlfcrafter\n```\n\n### Basic Usage\n\n```python\nfrom mlfcrafter import MLFChain, DataIngestCrafter, CleanerCrafter, ScalerCrafter, ModelCrafter, ScorerCrafter, DeployCrafter\n\n# Create ML pipeline in one line\nchain = MLFChain(\n    DataIngestCrafter(data_path=\"data/iris.csv\"),\n    CleanerCrafter(strategy=\"auto\"),\n    ScalerCrafter(scaler_type=\"standard\"),\n    ModelCrafter(model_name=\"random_forest\"),\n    ScorerCrafter(),\n    DeployCrafter()\n)\n\n# Run entire pipeline\nresults = chain.run(target_column=\"species\")\nprint(f\"Test Score: {results['test_score']:.4f}\")\n```\n\n## Requirements\n\n- **Python**: 3.8 or higher\n- **Core Dependencies**: pandas, scikit-learn, numpy, xgboost, joblib\n\n## Development\n\n### Setup Development Environment\n\n```bash\ngit clone https://github.com/brkcvlk/mlfcrafter.git\ncd mlfcrafter\npip install -r requirements-dev.txt\npip install -e .\n```\n\n### Run Tests\n\n```bash\n# Run all tests\npython -m pytest tests/ -v\n\n# Run tests with coverage  \npython -m pytest tests/ -v --cov=mlfcrafter --cov-report=html\n\n# Check code quality\nruff check .\n\n# Auto-fix code issues\nruff check --fix .\n\n# Format code\nruff format .\n```\n\n### Run Examples\n\n```bash\npython example.py\n```\n\n\n## Contributing\n\nWe welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Support\n\n- 📖 **Documentation**: [MLFCrafter Docs](https://brkcvlk.github.io/mlfcrafter/)\n- 🐛 **Bug Reports**: [GitHub Issues](https://github.com/brkcvlk/mlfcrafter/issues)\n- 💬 **Discussions**: [GitHub Discussions](https://github.com/brkcvlk/mlfcrafter/discussions)\n\n---\n\n**Made for the ML Community** \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbrkcvlk%2Fmlfcrafter","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbrkcvlk%2Fmlfcrafter","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbrkcvlk%2Fmlfcrafter/lists"}