rohanmistry231/Parkinsons-Disease-Classification
A Python-based machine learning project for classifying Parkinson's disease using patient data and algorithms like XGBoost and Random Forest. Includes data preprocessing, feature analysis, and model evaluation with Scikit-learn and Pandas for accurate predictions.
Leverages biomedical voice measurements (fundamental frequency, jitter, shimmer, harmonics-to-noise ratio) as the primary feature set for disease detection, extracted from the UCI Parkinson's dataset. Implements a Streamlit-based web interface for interactive predictions alongside batch model training, with model persistence via serialization to enable deployment without retraining.
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Python
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MIT
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May 24, 2025
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