Data Science to production accelerator
Project description
# Project Hadron ## Overview
Project Hadron is an open-source application framework for in-memory preprocessing, where data analysis, machine learning, and other data-intensive tasks require efficiency and speed. With :Apache Arrow as its canonical, and a more directed use of pandas, Project Hadron offers effective data management, extensive interoperability, improved memory management and hardware optimization.
At its concept, Project Hadron was conceived with a desire to improve the availability of objective relevant data, increase the transparency and traceability of data lineage and facilitate knowledge transfer, retrieval and reuse.
At its core Project Hadron is a selection of capabilities that represent an encapsulated set of actions that act upon a given set of features or dataset. An example of this would be FeatureSelection, a capability class, encapsulating cleaning data by removing uninformative columns.
For the complete documentation [read-the-docs](https://discovery-capability.readthedocs.io/en/latest/)
## Installation
### Python version We recommend using the latest version of Python. Project Hadron supports Python 3.8 and newer.
### Package installation The best way to install the component packages is directly from the [Python Package Index](https://pip.pypa.io/en/stable/) using pip.
The component package is discovery-capability and pip installed with:
`bash pip install discovery-capability `
if you want to upgrade your current version then using pip install upgrade with:
`bash pip install -U discovery-capability `
This will also install or update dependent third party packages. The dependencies are limited to Python, PyArrow and related Data manipulation tooling such as Pandas, Numpy, scipy, scikit-learn and visual packages matplotlib and seaborn, and thus have a limited footprint and non-disruptive installation in a data processing environment.
## Next Steps For next steps [read-the-docs](https://discovery-capability.readthedocs.io/en/latest/)
## License Distributed under the MIT License. See LICENSE.txt for more information or reference [MIT](https://choosealicense.com/licenses/mit/)
## Contributing
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag “enhancement”. Don’t forget to give the project a star! Thanks again!
Fork the Project
Create your Feature Branch (git checkout -b feature/AmazingFeature)
Commit your Changes (git commit -m ‘Add some AmazingFeature’)
Push to the Branch (git push origin feature/AmazingFeature)
Open a Pull Request
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