😸😹😺😻😼😽😾😿🙀🐱
GossipCat is a data science project 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 following features:
Agile machine learning framework: designed with a lean start and continuing improvement.
Pipeline data preprocessing: high cohesion, low coupling.
Algorithms comparison: provides a overview of multiple machine learning algorithms comparison.
Diverse model evaluation: makes the evaluation visible and with business sense.
Architectural thinking: not only data science but also machine learning engineering.
Story of the GossipCat
The package names after a cat of my friend, LEEverpool.
License
GossipCat is licensed under the Apache License 2.0. © Contributors, 2023.
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.
1 maintainer
Release files for gossipcat 0.3.36
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| gossipcat-0.3.36-py3-none-any.whl | Python 3 | none | any | Details |
Release files / gossipcat-0.3.36-py3-none-any.whl
| Download URL | gossipcat-0.3.36-py3-none-any.whl |
|---|---|
| Size | 81.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
d01cb5a2dcac00cb134ea59ce026384ce3637f39b7184643abb861f984e66b4d
|
|
BLAKE2b-256 checksum How to use checksums |
27cdfdb9c5dfcdfde3233e70a36215180bf4e85c7f0ec7a09b7dc24326c1ab7c
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.16
|
