Parkinson’s disease (PD) is the second most common neurodegenerative disease, affecting over 1 million people in the United States and over 8.5 million people globally. PD rates are increasing and the diagnosis rate has doubled over the last 25 years (WHO). With no cure in sight, PD remains a complex disease with difficult to predict disease progression. Certain biomarkers, such as combinations of 𝜶-synucleins and inflammation-related biomarkers tumor necrosis factor (TNF)-𝜶 and interleukins (IL) accumulate in PD patients but complex interactions between biomarkers make using them to predict disease progression a challenge (Eidson et al. 2017; Li and Le 2020). Our project aims to utilize data science principles and machine learning to create models that predict PD from demographic, MRI, and biomarker data obtained from PPMI [Parkinson’s Progression Markers Initiative].