Heart Disease Prediction

Project Description

This project aims to predict the presence of heart disease in patients using machine learning techniques. It is based on a dataset that includes various health features and is implemented using Python and popular libraries like Pandas, Scikit-learn, and Streamlit.

You can see a demo here

Features

• Heart disease prediction.
• Data visualization.
• Machine learning models implemented.
• Model performance evaluation.

Technologies Used

• Python
• Streamlit
• Pandas
• Scikit-learn
• Pickle

Installation

Usage

Clone the repository:

git clone https://github.com/ArielAyala/heart-disease-prediction-python.git

Navigate to the project directory:

cd heart-disease-prediction-python

Create a virtual environment:

python -m venv venv

Activate the virtual environment:

• Windows: venv\Scripts\activate
• Linux/macOS: source venv/bin/activate

To install the necessary dependencies, run the following command (ensure you have Python and pip installed on your system):

pip install -r requirements.txt

Run the app:

Streamlit run app-streamlit.py

Using Docker

Or just run the app using docker. You have a Dockerfile, docker-compose.yml, and .dockerignore file in your project root.

Build and run the Docker image:

docker-compose up -d