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Implimentations of Machine and Statistical Learning algorithms in python with added Utility, served with a Sklearn style interface

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Simple ML

Implimentations of Machine and Statistical Learning algorithms in python with added Utility, served with a Sklearn style interface. I've started this libary for practice of implimenting algorithms, but will be adding extra functionality sci-kit learn doesn't have out of box, like explainability functions, parallel training and GPU enablement

Initially appeared on gist

Getting Started

These instructions will give you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on deploying the project on a live system.

Prerequisites

Requirements for the software and other tools to build, test and push can be found in requirements.txt

Installing

TODDO- Write docker container deployment using requirements.txt

TODO- Deploy to pypi so that Library can be downloaded via

python -m pip install Simple_ML-

Running the tests

TODO- Write the automated tests for check cases using numpy arrays other than examples TODO- Impliment pandas dataframes to fit and predict models

Deployment

To use as part of larger codebase simply import Models from Models, for example-

from Models.supervised_models import Gaussian_Naive_Bayes

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

TODO- Impliment contributing.md

Authors

  • **Charlie Masters ** - Currently Sole contributer - LinkedIn

License

This project is licensed under the CC0 1.0 Universal Creative Commons License - see the LICENSE.md file for details

Acknowledgments

Please feel free to fork and contribute to appear down Here!

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