ASD Unsupervised Subgroup Discovery

This repository contains code for an unsupervised learning project aimed at identifying subgroups within the "abnormal" category of Autism Spectrum Disorder (ASD). The project uses data from 33 patients, each described by 200 features.

Objective

The main goal of this project is to apply unsupervised learning techniques to the provided patient data and discover hidden patterns and subgroups within the ASD "abnormal" group.

Features

• Utilizes Python and machine learning libraries like scikit-learn.
• Includes Jupyter notebooks for data preprocessing, clustering, and visualization.
• Provides insights into the nuances of autism patterns.

How to Use

1. Clone the repository: git clone https://github.com/yourusername/ASD_Unsupervised_Subgroup_Discovery.git
2. Navigate to the project directory: cd ASD_Unsupervised_Subgroup_Discovery
3. Install dependencies: pip install -r requirements.txt
4. Run the Jupyter notebooks in the specified order.

Contributing

Contributions are welcome! Feel free to submit issues or pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.