Decision tree for sleep disorder detection

About

This is a decision tree for sleep disorder detection, which was used in an Artificial Intelligence class taken in my Master's Degree.

Project Structure

decision-tree-impl/
├── src/                    # Source code
│   ├── fix_data.py        # Data preprocessing script
│   └── train_model.py     # Model training and evaluation script
├── data/                   # Dataset files
│   ├── original-dataset.csv
│   └── fixed-dataset.csv
├── output/                 # Results and outputs
│   └── result.txt
├── run.py                  # Main script to run the complete pipeline
└── README.md

Run

To run this project locally, you need to:

• Install and define a venv:

pip install virtualenv
virtualenv myenv

• Activate the virtual environment:

# On Windows
myenv\Scripts\activate

# On Linux/Mac
source myenv/bin/activate

• Install dependencies:

pip install pandas scikit-learn dtreeviz

• Run the complete pipeline:

python run.py

The run.py script will:

1. Preprocess the original dataset (data/original-dataset.csv)
2. Train the decision tree model
3. Evaluate the model and save results to output/result.txt

You can check the results in output/result.txt

Contributing

This repository is using Gitflow Workflow and Conventional Commits, so if you want to contribute:

• create a branch from develop branch;
• make your contributions;
• open a Pull Request to develop branch;
• wait for discussion and future approval;

I thank you in advance for any contribution.

Status

Finished

License

MIT