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.
data-science
r
random-forest
feature-selection
uci
regression-models
hyperparameter-tuning
parkinsons-disease
updrs-scale
xgboost-regression
shiny-r
parkinsons-telemonitoring-dataset
lasso-regularization
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Updated
May 12, 2024 - R