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 mlfcrafter

from 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.py

We 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