ML Pipeline Automation Tool - Chain together data processing, model training, and deployment with minimal code
Your support helps us continue developing and improving MLFCrafter for the ML community.
MLFCrafter 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.
- 🔗 Chainable Architecture - Connect multiple processing steps seamlessly
- 📊 Smart Data Handling - Automatic data ingestion from CSV, Excel, JSON
- 🧹 Intelligent Cleaning - Multiple strategies for missing value handling
- 📏 Flexible Scaling - MinMax, Standard, and Robust scaling options
- 🤖 Multiple Models - Random Forest, XGBoost, Logistic Regression support
- 📈 Comprehensive Metrics - Accuracy, Precision, Recall, F1-Score
- 💾 Easy Deployment - One-click model saving with metadata
- 🔄 Context-Based - Seamless data flow between pipeline steps
Writing 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.
Here’s why MLFCrafter might be the right tool for you:
✅ Automation without Black Box: You automate repetitive steps, but still keep visibility and control over each stage.
✅ 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.
✅ Readable & Reusable: The API is simple, clean, and built for easy experimentation and reproducibility.
✅ Scikit-learn Compatible: Use your favorite tools and estimators within the pipeline.
✅ Open for Extension: You can build your own custom crafters if needed.
✅ 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.
- Complete documentation is available -> MLFCrafter Docs
- Create your first Pipeline -> Your First Pipeline
- Start learning How Crafters work -> Crafters
- Do you want to see example usage, Check -> Example
pip install mlfcrafterfrom mlfcrafter import MLFChain, DataIngestCrafter, CleanerCrafter, ScalerCrafter, ModelCrafter, ScorerCrafter, DeployCrafter
# Create ML pipeline in one line
chain = MLFChain(
DataIngestCrafter(data_path="data/iris.csv"),
CleanerCrafter(strategy="auto"),
ScalerCrafter(scaler_type="standard"),
ModelCrafter(model_name="random_forest"),
ScorerCrafter(),
DeployCrafter()
)
# Run entire pipeline
results = chain.run(target_column="species")
print(f"Test Score: {results['test_score']:.4f}")- Python: 3.8 or higher
- Core Dependencies: pandas, scikit-learn, numpy, xgboost, joblib
git clone https://github.com/brkcvlk/mlfcrafter.git
cd mlfcrafter
pip install -r requirements-dev.txt
pip install -e .# Run all tests
python -m pytest tests/ -v
# Run tests with coverage
python -m pytest tests/ -v --cov=mlfcrafter --cov-report=html
# Check code quality
ruff check .
# Auto-fix code issues
ruff check --fix .
# Format code
ruff format .python example.pyWe welcome contributions! Please see our Contributing Guidelines for details.
This project is licensed under the MIT License - see the LICENSE file for details.
- 📖 Documentation: MLFCrafter Docs
- 🐛 Bug Reports: GitHub Issues
- 💬 Discussions: GitHub Discussions
Made for the ML Community