Parkinson's-disease-prediction

This repository presents a machine learning solution for predicting Parkinson's disease progression using the XGBoost algorithm. Parkinson's disease is a neurodegenerative disorder with a wide range of symptoms and progression rates. Leveraging features extracted from biomedical data, such as voice recordings and clinical assessments, this project employs the powerful XGBoost algorithm to classify patients into different disease progression stages. By analyzing patterns and correlations in the data, the model can assist in early diagnosis and personalized treatment planning for Parkinson's disease patients. The repository includes data preprocessing, model training, and evaluation, providing a robust framework for Parkinson's disease prediction using machine learning techniques.