A machine learning-based web app for detecting Parkinson's disease from voice recordings. The app extracts key voice features, applies pre-trained models, and provides real-time predictions of Parkinson's likelihood. Built using Streamlit, Librosa, and scikit-learn.

A machine learning web application built using Streamlit that predicts whether or not a patient has Diabetes, Heart Disease or Parkinsons.

This third project, part of a remote internship at Nexus Info, focuses on using machine learning to analyze and predict Parkinson's disease. The project involves developing and evaluating models to accurately diagnose the disease from various medical parameters.

This script processes the combined clinical, peptide, and protein data to train a machine learning model for predicting the severity of Parkinson's disease as measured by UPDRS scores. The script includes data preprocessing, exploratory data analysis, model training and evaluation, hyperparameter tuning, and SHAP values interpretation for the model

This is a Supervised Machine Learning model for predicting multiple diseases like heart disease, diabetes, etc.

🏥 An ML-powered tool that detects the likelihood of diabetes, heart disease, and Parkinson's based on user input health parameters.