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GossipCat, A Cat Who Is Always Gossiping.

Project description

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

  • Agile machine learning framework: designed with a lean start and continuing improvement.
  • Pipeline data preprocessing: high cohesion, low coupling.
  • Algorithm comparison: no free lunch in algorithm selection.
  • Diverse model evaluation: makes the evaluation with business sense and visible.
  • Architectural thinking: not just data science but also data engineering.

Story of the GossipCat

The package names after a cat of my friend, LEEverpool.

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

License

GossipCat is licensed under the Apache License 2.0. © Contributors, 2019.

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


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