Metadata-Version: 2.1
Name: pneuro
Version: 1.0.5.2.5
Summary: Python API for Automated ML
Home-page: http://autoneuro.ml
Author: iNeuron
Author-email: 
Maintainer: iNeuron
Maintainer-email: support@iNeuron.com
License: MIT
Project-URL: Documentation, https://github.com/nabeelfahmi12/AutoNeuro-Documentation
Project-URL: Source Code, https://github.com/viratsagar/Autoneuro
Description: [![Python](https://img.shields.io/badge/python-3%20%7C%203.7-blue)](https://pypi.org/project/pneuro)
        [![PyPI version](https://badge.fury.io/py/pneuro.svg)](https://badge.fury.io/py/pneuro)
        
        <h1> AutoNeuro</h1>
        
        <div align="center">
          <img src="https://2s7gjr373w3x22jf92z99mgm5w-wpengine.netdna-ssl.com/wp-content/uploads/2020/02/Qeexo_auto_ML.png">
        </div>
        
        AutoNeuro is an automated machine learning application built using python 3.7. It allows users to build production ready ML models with ease and efficiency.See the [About us](https://nabeelfahmi12.github.io/AutoNeuro-Documentation/GettingStarted/GettingStarted/) page for more information.
        
        website: https://nabeelfahmi12.github.io/AutoNeuro-Documentation/
        
        
        ## Installation
        
        ### Dependencies
        
        AutoNeuro requires:
        * Python 3 
        * Numpy
        * Pandas
        * scikit-learn
        <br>
        
        ## User Installation
        If you already have a working installation of numpy and Pandas, the easiest way to install autoNeuro is using ```Pip```
        ```
        pip install pneuro
        ```
        
        The documentation includes more detailed [installation instructions](https://nabeelfahmi12.github.io/AutoNeuro-Documentation/GettingStarted/How%20to%20use/)
        
        ## Change log
        See the [change log]() for a history of notable changes to AutoNeuro.
        
        ## Development
        We welcome new contributors of all experience levels. The [Development Guide](https://nabeelfahmi12.github.io/AutoNeuro-Documentation/ForDevelopers/MethodsforModelbuilding/) has detaled information about contributing code, documentation, tests, and more. 
        We have included some basic information in README.
        
        ### Important links
        * Official source code repo: https://github.com/viratsagar/Autoneuro
        * Download releases: 
        * Issue Tracker:
        
        ### Source code
        You can check the latest sources with the command:
        ```
        git clone https://github.com/nabeelfahmi12/AutoNeuro-Documentaion.git
        ```
        ### Contributing
        To learn more about making a contribution to scikit-learn, please see our
        [Contributing guide](https://nabeelfahmi12.github.io/AutoNeuroDocumentation/Contributingtoautoneuro/Contributingtoautoneuro/)
        
        ### Testing
        After installation, you can launch the test suite from outside the source directory (you will need to have pytest >= 5.0.1 installed):
        ```
        pytest AutoNeuro
        ```
        ### Submitting a Pull Request
        Before opening a Pull Request, have a look at the full Contributing page to make sure your code 
        complies with our guidelines: https://nabeelfahmi12.github.io/AutoNeuro-Documentation/Contributingtoautoneuro/Contributingtoautoneuro/
        
        ## Help and Support
        https://nabeelfahmi12.github.io/AutoNeuro-Documentation/Contributingtoautoneuro/Contributingtoautoneuro/
        
        ### Documentation
        * HTML documentation (stable release):
        * HTML documentation (development version):
        * FAQ: 
        ### Communication
        * Mailing list:
        * Twitter: https://twitter.com/Sudhans74624324
        * Stack Overflow: 
        * Website: https://nabeelfahmi12.github.io/AutoNeuro-Documentation/
        
        ## Citation
        If you use autoneuro in a scientific publication, we would appreciate citations: 
        
        
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers 
Classifier: Intended Audience :: End Users/Desktop
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development 
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: examples
Provides-Extra: examples_unix
