https://github.com/soumyapro/parkinson-disease-prediction

This project predicts Parkinson's disease using machine learning models.
https://github.com/soumyapro/parkinson-disease-prediction

logistic-regression numpy pandas scikit-learn svc xgboost

Last synced: 8 months ago
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This project predicts Parkinson's disease using machine learning models.

• Host: GitHub
• URL: https://github.com/soumyapro/parkinson-disease-prediction
• Owner: Soumyapro
• Created: 2024-08-22T05:17:34.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-08-22T05:27:26.000Z (about 1 year ago)
• Last Synced: 2025-02-13T17:18:03.058Z (9 months ago)
• Topics: logistic-regression, numpy, pandas, scikit-learn, svc, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 668 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Parkinson's Disease Prediction

![Parkinson's Disease](parkinson.jpg)

## Overview

This project predicts Parkinson's disease using machine learning models.

## Table of Contents

- [Installation](#installation)

- [Models](#models)

## Installation

To get started, clone the repository and install the required dependencies.

## Models

1. **Logistic Regression**: A basic linear model used for binary classification.

2. **XGBoost**: An ensemble method known for its performance and efficiency in classification tasks. It provided the best results for this project.

3. **Support Vector Classifier (SVC)**: A powerful classifier that works well for both linear and non-linear problems.

Among the models tested, **XGBoost** yielded the best results in terms of classification metrics.

### Clone the Repository

```bash

git clone https://github.com/Soumyapro/parkinsons-disease-prediction.git

cd parkinsons-disease-prediction