Skip to main content

Python API for Automated ML

Project description

Python PyPI version

AutoNeuro

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 page for more information.

website: https://nabeelfahmi12.github.io/AutoNeuro-Documentation/

Installation

Dependencies

AutoNeuro requires:

  • Python 3
  • Numpy
  • Pandas
  • scikit-learn

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

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 has detaled information about contributing code, documentation, tests, and more. We have included some basic information in README.

Important links

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

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

Citation

If you use autoneuro in a scientific publication, we would appreciate citations:

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pneuro-1.3.7.0.0.tar.gz (1.0 MB view details)

Uploaded Source

Built Distribution

pneuro-1.3.7.0.0-py3-none-any.whl (1.1 MB view details)

Uploaded Python 3

File details

Details for the file pneuro-1.3.7.0.0.tar.gz.

File metadata

  • Download URL: pneuro-1.3.7.0.0.tar.gz
  • Upload date:
  • Size: 1.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/49.2.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.4

File hashes

Hashes for pneuro-1.3.7.0.0.tar.gz
Algorithm Hash digest
SHA256 4f41cdcc19ac97fe1cdff53d2487bba5eeb19012aa1620aba6de9fc496327060
MD5 9207fa9ee3367f525a0c699e64a5bcdc
BLAKE2b-256 f99c8894cb3940ce665d00974d945b5281b79b603473f14b7bfd41bebeaa731d

See more details on using hashes here.

File details

Details for the file pneuro-1.3.7.0.0-py3-none-any.whl.

File metadata

  • Download URL: pneuro-1.3.7.0.0-py3-none-any.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/49.2.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.4

File hashes

Hashes for pneuro-1.3.7.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 55f40b87aa8c014e6cfef191e42e79c19facb4e5998298f0ef9e081a3fdc95f1
MD5 8a0462ffb76091a0dd1237d31e77e0e7
BLAKE2b-256 2f20ba17ef8c848b043a1dffea5bc67e2e9f4301181f00340cbac405042f9b2a

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page