Metadata-Version: 2.1
Name: dynapipe
Version: 0.2.0
Summary: Dynamic Pipeline is a high-level API to help data scientists building models in ensemble way, and automating Machine Learning workflow with simple coding.
Home-page: https://github.com/tonyleidong/DynamicPipeline
Author: Tony Dong
Author-email: tonyleidong@gmail.com
License: UNKNOWN
Description: ## Dynamic Pipeline
        [![PyPI Latest Release](https://img.shields.io/pypi/v/dynapipe)](https://pypi.org/project/dynapipe/)
        [![Github Issues](https://img.shields.io/github/issues/tonyleidong/DynamicPipeline)](https://github.com/tonyleidong/DynamicPipeline/issues)
        [![License](https://img.shields.io/github/license/tonyleidong/DynamicPipeline)](https://github.com/tonyleidong/DynamicPipeline/blob/master/LICENSE)
        [![Last Commit](https://img.shields.io/github/last-commit/tonyleidong/DynamicPipeline)](https://github.com/tonyleidong/dynapipe)
        [![Python Version](https://img.shields.io/pypi/pyversions/dynapipe)](https://pypi.org/project/dynapipe/)
        
        
           
        #### Author: [Tony Dong](http://www.linkedin.com/in/lei-tony-dong)
        
        <img src="https://github.com/tonyleidong/DynamicPipeline/blob/master/docs/DynamicPipeline_Official_Logo.png" width="80">**Dynamic Pipeline** is a high-level API to help data scientists building models in ensemble way, and automating Machine Learning workflow with simple code.
        
        Documentation:  https://dynamic-pipeline.readthedocs.io/
        
        ### Current available modules: 
         - autoPP for feature preprocessing
         - autoFS for classification/regression features selection
         - autoCV for classification/regression model selection and evaluation
         - autoPipe for modules automatic pipeline connection & generate model's performance reports
         
        ### Modules in development:
         - autoVIZ for pipeline visualization 
         - autoFlow for tracking and deployment
         - autoTM for text mining
         - Unsupervised models specific modules
Keywords: auto machine learning,features selection,model selection,model preprocessing,pipeline,ensemble modeling
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
