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https://github.com/yinsn/ParaDance
Offers a toolset for comprehensive, multi-faceted large-scale data analysis and optimizations
https://github.com/yinsn/ParaDance
Last synced: 6 days ago
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Offers a toolset for comprehensive, multi-faceted large-scale data analysis and optimizations
- Host: GitHub
- URL: https://github.com/yinsn/ParaDance
- Owner: yinsn
- License: mit
- Created: 2023-05-23T06:46:39.000Z (over 1 year ago)
- Default Branch: develop
- Last Pushed: 2024-10-30T14:57:55.000Z (9 days ago)
- Last Synced: 2024-10-30T15:47:55.679Z (9 days ago)
- Language: Python
- Homepage:
- Size: 1.82 MB
- Stars: 40
- Watchers: 4
- Forks: 15
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ParaDance
[![Python](https://img.shields.io/badge/python3.9-red?logo=Python&logoColor=white)](https://www.python.org)
[![PyPI](https://img.shields.io/pypi/v/paradance?color=green)](https://pypi.org/project/paradance/)
[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/yinsn/paradance)
[![Downloads](https://static.pepy.tech/badge/ParaDance)](https://pepy.tech/project/ParaDance)
[![Downloads](https://static.pepy.tech/badge/ParaDance/month)](https://pepy.tech/project/ParaDance)ParaDance is a comprehensive Python toolkit tailored for large-scale data analysis. It offers functionalities for data loading, processing, evaluation metrics, sampling, and visualization. Uniquely, ParaDance features multi-objective Bayesian optimization, supporting various aggregation formulas and calculators. This flexibility in multi-objective settings positions ParaDance as a valuable tool for data scientists and researchers.
## Installation
You can install ParaDance using pip:
```bash
pip install --upgrade paradance
```## Contributing
We welcome contributions! If you're interested in enhancing the features of ParaDance, please create a pull request.
## License
ParaDance is licensed under the MIT License.
## Acknowledgments
We appreciate the community for your support and contributions. Thank you!