https://github.com/gawadx1/heart-disease-prediction

heart disease prediction by Python
https://github.com/gawadx1/heart-disease-prediction

heart-disease-prediction knn-classification machine-learning machine-learning-algorithms python random-forest-classifier scikit-learn scikitlearn-machine-learning svm svm-classifier

Last synced: 2 days ago
JSON representation

heart disease prediction by Python

• Host: GitHub
• URL: https://github.com/gawadx1/heart-disease-prediction
• Owner: gawadx1
• License: mit
• Created: 2024-05-16T10:17:20.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-12-23T07:26:38.000Z (17 days ago)
• Last Synced: 2024-12-23T07:32:46.267Z (17 days ago)
• Topics: heart-disease-prediction, knn-classification, machine-learning, machine-learning-algorithms, python, random-forest-classifier, scikit-learn, scikitlearn-machine-learning, svm, svm-classifier
• Language: Jupyter Notebook
• Homepage:
• Size: 451 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Heart Disease Prediction

This project aims to develop a machine learning model to predict the likelihood of heart disease in patients using the Scikit-Learn library. The model utilizes various health metrics and patient data to provide insights and predictions.

## GUI

Below is a screenshot of the graphical user interface (GUI):

![Project GUI](https://github.com/user-attachments/assets/667dc14b-b8a2-4a82-a17e-50ed512e967f)

## Table of Contents

- [Project Overview](#project-overview)

- [Technologies Used](#technologies-used)

- [Dataset](#dataset)

- [Installation](#installation)

## Project Overview

Heart disease is one of the leading causes of death worldwide. Early detection and prediction of heart disease can significantly reduce mortality rates. This project uses a dataset to train a machine learning model that predicts the likelihood of heart disease based on various features such as age, cholesterol levels, and blood pressure.

## Technologies Used

- Python

- Scikit-Learn

- Pandas

- NumPy

- Matplotlib

- Seaborn

- Jupyter Notebook

## Dataset

The dataset used for this project is the **UCI Heart Disease Dataset**, which can be found [here](https://archive.ics.uci.edu/ml/datasets/heart+Disease). The dataset consists of various attributes, including:

- Age

- Sex

- Chest Pain Type

- Resting Blood Pressure

- Serum Cholesterol

- Fasting Blood Sugar

- Resting Electrocardiographic Results

- Maximum Heart Rate Achieved

- Exercise Induced Angina

- Oldpeak

- Slope of the Peak Exercise ST Segment

- Number of Major Vessels

- Thalassemia

- Target (Heart Disease Presence)

## Installation

To run this project, ensure you have Python installed on your machine. You can create a virtual environment and install the required libraries as follows:

```bash

# Clone the repository

git clone https://github.com/gawadx1/Heart-Disease-Prediction.git

cd Heart-Disease-Prediction

# Create a virtual environment (optional)

python -m venv venv

source venv/bin/activate  # On Windows use `venv\Scripts\activate`

# Install required packages

pip install -r requirements.txt