Parkinson’s Disease Detection Using Machine Learning

Millions of people worldwide suffer with Parkinson's disease (PD), a progressive neurological illness.One of the biggest challenges in diagnosing Parkinson's disease (PD) is still early detection, which is why machine learning (ML) techniques are being investigated.Using machine learning (ML) techniques such as logistic regression, support vector machines (SVM), random forests, gradient boosting, K-Nearest Neighbors (KNN), and XGBoost, the goal of this project is to construct a system for detecting Parkinson's illness.These algorithms are trained and assessed to determine which model best separates persons with and without Parkinson's disease.They do this by using a dataset that includes voice-based biomarkers from both affected and healthy subjects.The application of Exploratory Data Analysis (EDA) approaches facilitates feature selection and preprocessing stages that are essential for model creation by providing insights into the distribution, correlations, and underlying patterns of the data.