https://github.com/brkcvlk/mlfcrafter
ML Pipeline Automation Tool - Chain together data processing, model training, and deployment with minimal code. Build production-ready ML workflows in minutes, not hours.
https://github.com/brkcvlk/mlfcrafter
ai automation automl beginner-friendly data-processing data-science framework library machine-learning ml-framework mlops model-training pipeline production-ready python python3 scikit-learn toolkit workflow xgboost
Last synced: 5 months ago
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ML Pipeline Automation Tool - Chain together data processing, model training, and deployment with minimal code. Build production-ready ML workflows in minutes, not hours.
- Host: GitHub
- URL: https://github.com/brkcvlk/mlfcrafter
- Owner: brkcvlk
- License: mit
- Created: 2025-07-24T20:59:02.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-12-15T09:28:06.000Z (6 months ago)
- Last Synced: 2025-12-15T16:19:12.451Z (6 months ago)
- Topics: ai, automation, automl, beginner-friendly, data-processing, data-science, framework, library, machine-learning, ml-framework, mlops, model-training, pipeline, production-ready, python, python3, scikit-learn, toolkit, workflow, xgboost
- Language: Python
- Homepage: https://brkcvlk.github.io/MLFCrafter/
- Size: 270 KB
- Stars: 4
- Watchers: 1
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
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README
MLFCrafter
> **ML Pipeline Automation Tool - Chain together data processing, model training, and deployment with minimal code**
[](https://pypi.org/project/mlfcrafter/)
[](https://pypi.org/project/mlfcrafter/)
[](https://github.com/brkcvlk/mlfcrafter/actions)
[](https://brkcvlk.github.io/mlfcrafter/)
[](LICENSE)
[](https://pepy.tech/projects/mlfcrafter)
---
## ⭐ **If you find MLFCrafter useful, please consider starring this repository!**
Your support helps us continue developing and improving MLFCrafter for the ML community.
---
## What is MLFCrafter?
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.
### Key Features
- **🔗 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
## Why MLFCrafter?
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.
---
## Documentation
- Complete documentation is available -> [MLFCrafter Docs](https://brkcvlk.github.io/MLFCrafter/)
- Create your first Pipeline -> [Your First Pipeline](https://brkcvlk.github.io/MLFCrafter/getting-started/first-pipeline/)
- Start learning How Crafters work -> [Crafters](https://brkcvlk.github.io/MLFCrafter/api/crafters/data-ingest-crafter/)
- Do you want to see example usage, Check -> [Example](https://brkcvlk.github.io/MLFCrafter/examples/basic-usage/)
## Quick Start
### Installation
```bash
pip install mlfcrafter
```
### Basic Usage
```python
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}")
```
## Requirements
- **Python**: 3.8 or higher
- **Core Dependencies**: pandas, scikit-learn, numpy, xgboost, joblib
## Development
### Setup Development Environment
```bash
git clone https://github.com/brkcvlk/mlfcrafter.git
cd mlfcrafter
pip install -r requirements-dev.txt
pip install -e .
```
### Run Tests
```bash
# 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 .
```
### Run Examples
```bash
python example.py
```
## Contributing
We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Support
- 📖 **Documentation**: [MLFCrafter Docs](https://brkcvlk.github.io/mlfcrafter/)
- 🐛 **Bug Reports**: [GitHub Issues](https://github.com/brkcvlk/mlfcrafter/issues)
- 💬 **Discussions**: [GitHub Discussions](https://github.com/brkcvlk/mlfcrafter/discussions)
---
**Made for the ML Community**