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Visualization & Classification of a 13 dimensional wine data using an unsupervised learning technique called Self Organizing Maps. We map a 13D data into a 2D grid and used color profile to differentiate data belonging to each class.

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13-D Wine data visualization & classification using 2D Self Organizing Maps.
Explore Self Organizing Maps docs »
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Self Organizing Map - An Unsupervised learning technique to visualize and classify 13 dimensional wine data by mapping it to a 2D map.

Table of contents

Quick start

There are two ways to run this app:

  • Download the latest Docker container. and run from the app in that container ( NOte: No Docker container at the moment)
  • Clone the repo:
    git clone https://github.com/Sudharsan10/self-organizing-maps.git

Status

Documentation Status Maintenance made-with-python pip-version pyqt-version pyqt-version pyqt-version numpy-version made-with-Markdown contributors Logo

What's included

Within the download you'll find the following directories and files, logically grouping the modules in its own packages. You'll see something like this:

Self Organizing Maps/   
├── data/
|   ├── Processed/...
|   └── Raw/...
├── docs/ ...
├── img/ ...
├── notebooks/
|   ├── __init__.py
|   ├── test_tile_puzzle_solver.py 
|   └── tile_puzzle_solver.py
├── src/
|   ├── handler/
|   |   ├── __init__.py
|   |   └── utility.py
|   └── som
|       ├── __init__.py
|       ├── som.py
|       └── som2d.py
├── Readme.md
├── main.py
└── setup.py

Note: Few of the sections are under progress, if you need info on those sections please feel free to reach out to me s10@terpmail.umd.edu

Bugs and feature requests

Have a bug or a feature request? Search for existing and closed issues, if your problem or idea is not addressed yet, please open a new issue.

Creators

@Sudharsan : https://github.com/Sudharsan10

Thank you for stopping by!

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Visualization & Classification of a 13 dimensional wine data using an unsupervised learning technique called Self Organizing Maps. We map a 13D data into a 2D grid and used color profile to differentiate data belonging to each class.

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