Added a powerful new tool to the Parkinson's disease prediction model…

…: Gradient Boosting. This method boosts accuracy by combining predictions from multiple models. I specifically chose XGBoost for its efficiency and strong performance in machine learning.

The Gradient Boosting algorithm, specifically XGBoost, has been added to the model pipeline for Parkinson's disease detection.
This algorithm leverages the power of ensemble learning by sequentially building a series of decision trees, where each tree corrects the errors of the previous ones.
The model finally gives the accuracy of 94%.