Computer Intelligence subject final project at UPC.

This repository focuses on two machine learning projects in the healthcare domain.

This R-based data science project on the UCI Parkinson's dataset employs machine learning (Decision tree, Random Forest, SVM, XGBoost) with a focus on hyperparameter tuning and feature selection. This repository showcases insights into Parkinson's disease prediction using effective data science practices.

Our goal is to create a device that will utilize patient’s daily actions instead of choreographed actions to monitor and quantify Parkinson’s disease based on part of the motor control section of the UPDRS scale.

Data preprocessing and visualization of 5,875 voice recordings from Parkinson's patients to explore predictors of UPDRS scores.