Using Different Machine Learning Models predictions are made for the presence of Parkinson's Disease in An Individual. The Dataset can be found in Kaggle.

Basics of machine learning is END-TO-END Repository which includes very Basic Machine Learning Models and Notebook

Model to predict Parkinson's disease in patients

The objective of this projects is to build a model to accurately detect the presence of Parkinson’s disease in an individual.

Project for explaining Parkinson's Dataset variance though PCA, and detecting Parkinson's with XGBoost

This contains a few projects that were allotted during the Data Science and Machine Learning Internship at The Intern Academy in July, 2021.

Computer Vision project such as Image Preprocessing, Image Feature Extraction, Object Detection

Parkinson Detection Project

Python Machine Learning Project

Final project for Signal Processing course that focuses on Parkinson's Disease detection.

Neurovoz corpus of parkinosnian speech

Logistic Regression in the Diagnosis of Parkinson's Disease

Multiple Disease Prediction Webapp using MachineLearning Deployed using steamlit

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

Implemented Several ML Techniques for Parkinson’s Detection with Speech Signals- Machine Learning Course Project

Code to process the fNIRS data of Parkinson's Disease Patients and matched controls available in Guevara, E., et al. Zenodo. https://doi.org/10.5281/zenodo.7966830

Parkinsons-Disease-Detection This is a project which predicts whether a person have Parkinson's Disease or not

Trabajo de fin grado en el que se ha desarrollado una aplicación web para ayudar tanto a médicos como a pacientes de la enfermedad de Párkinson a poder monitorizar su evolución de una forma fácil y rápida.