EduGAN

Education Data GAN
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Table of Contents

• About the Project
  • Built With
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Roadmap
• Contributing
• License
• Contact
• Acknowledgements

About The Project

Generative Adversarial Network designed for education data generation.

Create synthetic classes that resemble a real class without recreating actual samples.

Distribute class datasets without compromising privacy and security of actual data.

Built With

• Python
• TensorFlow
• Pandas
• numpy
• Others can be found in requirements.txt

Getting Started

To get a local copy up and running follow these simple steps.

Installation

1. Clone the repo

git clone https://github.com/ILXL/EduGAN

2. Install pip packages

pip3 install -r requirements.txt

Usage

1. Convert raw data into CSV format
2. Clean CSV data and keywords
* Example available in DataProcessor.py
3. Add JSON parameters into DataInformation.json
4. Change keyword in GAN_Test.py to desired key from DataInformation.json
5. Run GAN_Test.py

Roadmap

• Add support for categorical features
• Create UI for visualization and adjustment in-between training

Contributing

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

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

License

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

Contact

Peter Bautista - [email protected]

Project Link: https://github.com/pbaut002/EduGAN

Acknowledgements

• Paul Salvador Inventado, PhD