This repository contains code and resources for detecting Parkinson's disease using machine learning techniques. The project aims to leverage various data processing and modeling techniques to accurately diagnose Parkinson's disease from biomedical voice measurements.

• Overview
• Dataset
• Installation
• Usage
• Models
• Results
• Contributing
• License

Parkinson's disease is a progressive neurological disorder that affects movement. Early and accurate detection is crucial for managing the disease effectively. This project focuses on building a machine learning model to classify individuals as having Parkinson's disease or not, based on voice measurements.

The dataset used in this project is from the UCI Machine Learning Repository and contains biomedical voice measurements from 31 people, 23 with Parkinson's disease. The features include various voice recordings and measurements, such as fundamental frequency, jitter, shimmer, and harmonic-to-noise ratio.

• Dataset Source: Parkinson's Disease Data Set

To get started with this project, clone the repository and install the necessary dependencies.

git clone https://github.com/yourusername/parkinsons-disease-detection.git
cd parkinsons-disease-detection
pip install -r requirements.txt

Before training the model, preprocess the data to ensure it is clean and ready for modeling.

python preprocess.py

Train the machine learning model using the preprocessed data.

python train.py

Evaluate the trained model's performance on the test data.

python evaluate.py

Use the trained model to make predictions on new data.

python predict.py --input data/new_data.csv

The project explores various machine learning models to identify the best performing model for Parkinson's disease detection. The models include:

• Logistic Regression
• Support Vector Machine (SVM)
• Random Forest
• Gradient Boosting
• Neural Networks

The performance of each model is evaluated using metrics such as accuracy, precision, recall, and F1-score. The results are documented in the results directory.

Contributions are welcome! If you have any ideas or improvements, feel free to open an issue or submit a pull request.

1. Fork the repository.
2. Create a new branch: git checkout -b feature-branch-name.
3. Make your changes and commit them: git commit -m 'Add some feature'.
4. Push to the branch: git push origin feature-branch-name.
5. Open a pull request.

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

Feel free to reach out with any questions or suggestions!