anddddrew / Abacus

MNIST classification w/ Scikit-Learn; deployed to Flask

Home Page:abacus-two.vercel.app

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool


🧮Abacus🧮

MNIST classification webapp!
Explore the docs »

View Demo · Report Bug · Request Feature

Table of Contents
  1. About The Project
  2. </li>
    <li><a href="#usage">Usage</a></li>
    <li><a href="#roadmap">Roadmap</a></li>
    <li><a href="#contributing">Contributing</a></li>
    <li><a href="#license">License</a></li>
    <li><a href="#contact">Contact</a></li>
    <li><a href="#acknowledgments">Acknowledgments</a></li>
    

About The Project

This project is my first end-to-end Machine Learning project, featuring the MNIST dataset. I used the K-Nearest Neighbors Algorithm to classify the dataset with 97.55% precision, tuning the hyperparameters with Grid Search and preventing overfitting and underfitting via Grid Search. This model is then saved to a compressed pickle file where I use Flask to route it to a frontend webapp that's styled with TailwindCSS.

Built With

This section should list any major frameworks/libraries used to bootstrap your project. Leave any add-ons/plugins for the acknowledgements section. Here are a few examples.

(back to top)

(back to top)

Roadmap

  • Create initial model
  • Reach 97% precision w/ Grid Search
  • Setup Flask backend
  • Create frontend
  • Deploy to Vercel
  • Dockerize application

See the open issues for a full list of proposed features (and known issues).

(back to top)

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

(back to top)

License

Distributed under the MIT License. See LICENSE.txt for more information.

(back to top)

Contact

Twitter - @Neclo0

Discord - Neclo#6412

Project Link: https://github.com/Necl0/Abacus

(back to top)

About

MNIST classification w/ Scikit-Learn; deployed to Flask

abacus-two.vercel.app

License:MIT License


Languages

Language:Python 61.6%Language:HTML 34.9%Language:Dockerfile 3.5%