Detecting Parkinson's Disease Using Vocal Features

The report explores using machine learning to predict Parkinson’s Disease based on vocal features, aiming for non-invasive early diagnosis.It details preprocessing steps like handling class imbalance with SMOTE and standardizing features, followed by EDA revealing key vocal differences in patients.Multiple models (Random Forest, SVC, Gradient Boosting) were evaluated, with Random Forest achieving the highest accuracy (94%) and performance metrics.Findings highlight vocal frequency and amplitude variations as critical predictors, supported by visualizations like boxplots and correlation heatmaps. https://github.com/alihassan098799/datastewardship_1