Skip to main content

Simplify the process of machine learning from data cleaning, simple feature engineering, parameter tuning, to results output.

Project description

😸😹😺😻😼😽😾😿🙀🐱

GossipCat is a machine learning framework that simplifies the process of machine learning from data cleaning, simple feature engineering, hyper parameter tuning, to results outputing. It is designed to be efficient with the following features:

  • Combine the feature engineering and parameter tuning.

  • Automate parameter tuning with algorithms.

  • Provide accesses of the highist efficient machine learning algorithms.

Story of the GossipCat

The package names after a cat of my friend, LEEverpool. Actually, the GossipCat is the name of a WeChat group, where my friends gossip there.

https://raw.githubusercontent.com/Ewen2015/GossipCat/master/GossipCat.jpeg

License

GossipCat is licensed under the Apache License 2.0. © Contributors, 2017.

A permissive license whose main conditions require preservation of copyright and license notices. Contributors provide an express grant of patent rights. Licensed works, modifications, and larger works may be distributed under different terms and without source code.

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distribution

gossipcat-0.1.8.tar.gz (10.2 kB view hashes)

Uploaded Source

Built Distribution

gossipcat-0.1.8-py3-none-any.whl (7.2 kB view hashes)

Uploaded Python 3

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