Machine Learning in R for Beginners | Linear Regression vs Random Forest | Real Parkinson's Data

Learn how to build and evaluate machine learning regression models in R using a real healthcare dataset containing 5,875 voice recordings from 42 Parkinson's disease patients. In this beginner-friendly tutorial, we predict Parkinson's disease severity (total_UPDRS) using Age, Sex, HNR, and PPE. Topics covered: • Explore and visualize real Parkinson's disease data. • Understand repeated patient recordings. • Split data into training (80%) and testing (20%) by patient. • Build a Linear Regression prediction model. • Train a Random Forest model using 500 trees. • Predict Parkinson's disease severity on unseen patients. • Evaluate models using RMSE, MAE, and R-squared. • Compare machine learning performance using graphs. Dataset: UCI Parkinson's Telemonitoring https://archive.ics.uci.edu/dataset/1... Software: R / RStudio Package: ranger For further assistance: fayyazm3@gmail.com