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Heart-Disease-Prediction

(★ 53)

A Python project to predict heart disease

File Explorer

Download Latest Version (.zip)

- - index.html
- app.py
- heart.csv
- model.pkl
- README.md
- requirements.txt
- train_model.py
README.md

# Installation Guide

1. Get the code

git clone https://github.com/Nivetha07-web/Heart-Disease-Prediction

Downloads the entire project code from GitHub to your computer.

cd Heart-Disease-Prediction

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r Heart-Disease-Prediction/requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

Runs the Python script (or module).

✅ If it runs without errors and prints output in the terminal, it worked.

// repository documentation

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# Heart Disease Prediction App

A Machine Learning based web application that predicts the likelihood of heart disease using patient medical data.

## Project Overview

This project uses a trained Machine Learning model to predict whether a person is likely to have heart disease based on various health parameters.

The model is built using Python and deployed using Flask as a web application.

## Features

- Machine Learning model trained on heart disease dataset
- Web interface using Flask
- User-friendly input form
- Real-time prediction result
- Model accuracy: ~88%

## Input Parameters

The app uses the following medical attributes:

- Age
- Sex
- Chest Pain Type (cp)
- Resting Blood Pressure (trestbps)
- Cholesterol (chol)
- Fasting Blood Sugar (fbs)
- Rest ECG (restecg)
- Max Heart Rate (thalach)
- Exercise Induced Angina (exang)
- Oldpeak
- Slope
- Number of Major Vessels (ca)
- Thal

## Technologies Used

- Python
- Pandas
- NumPy
- Scikit-learn
- Flask
- HTML

## Project Structure

Heart-Disease-Prediction/
│
├── app.py
├── train_model.py
├── model.pkl
├── heart.csv
│
└── templates/
    └── index.html

## How to Run the Project

1. Install required libraries:

pip install flask pandas numpy scikit-learn

2. Train the model:

python train_model.py

3. Run the Flask app:

python app.py

4. Open in browser:

http://127.0.0.1:5000

## Future Improvements

- Improve UI design
- Deploy online (Render / Railway / Heroku)
- Add data validation
- Add visualization dashboard