Exploring Parkinsons Disease Progression Through Python-Based Data Analysis Techniques

Parkinson’s disease is a long-term neurological illness that mainly influences motor functions balance, and speaking abilities. This study aims to present a reproducible Python-based EDA framework to understand the progressive growth of Parkinson’s disease using an authentic dataset comprising500affectedpatients. Withtheuseofmajorpython libraries, ExploratoryDataAnalysis(EDA)methodswereappliedtoclean,processandvisualizethedata.Thestudyinvolvedexploring correlation between features like UPDRS score, motor function, speech difficulty and years since diagnosis. Descriptive statistics, correlation heatmaps and visual plots helped in recognizing symptom patterns and perspective-related progression of this condition. This work exhibits how EDAcan be useful in rapid analysis of clinical data, chiefly in emphasizing the role of clinical characteristicsincomplicateddisorderslikeParkinson’sdisease.Ourworkflowidentifiessymptom clusters and temporal trends, offering a template for clinical data analysis.