Heart Disease Prediction 🫀

A machine learning-based web application to predict the likelihood of heart disease based on user inputs such as age, chest pain type, and maximum heart rate achieved.

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Table of Contents 📚

• Introduction
• Features
• Technologies Used
• Installation
• Usage
• Live Demo
• Contributing
• License

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Introduction 🚀

This project is a web application that uses a machine learning model to predict the presence of heart disease. The model is trained on a dataset containing features like age, chest pain type, and maximum heart rate. The application is built using Flask for the backend, HTML/CSS for the frontend, and a pre-trained logistic regression model for predictions.

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Features ✨

• User-friendly Interface: Simple and intuitive form for inputting data.
• Real-time Prediction: Instantly predicts the likelihood of heart disease.
• Responsive Design: Works seamlessly on both desktop and mobile devices.
• Error Handling: Provides clear error messages for invalid inputs.

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Technologies Used 🛠️

• Frontend: HTML, CSS, JavaScript (jQuery)
• Backend: Flask (Python)
• Machine Learning: Scikit-learn (Logistic Regression)
• Model Serialization: Joblib
• Deployment: (Add deployment platform if applicable, e.g., Heroku, AWS, etc.)

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Installation 🛠️

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/juned-k786/HeartDisease-Detection-ML.git
   cd heart-disease-prediction

Set up a virtual environment:

bash Copy python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate Install dependencies:

bash Copy pip install -r requirements.txt Run the Flask application:

bash Copy python app.py Open your browser and navigate to https://heartdisease-pwnu.onrender.com/

Usage 🖥️ Enter the required details:

Age: Your age in years.

Chest Pain Type: A value between 0 and 3 representing the type of chest pain.

Max Heart Rate Achieved: Your maximum heart rate during exercise.

Click the Predict button to see the result.

Live Demo 🌐 Live Demo

Contributing 🤝 Contributions are welcome! If you'd like to contribute, please follow these steps:

Fork the repository.

Create a new branch (git checkout -b feature/YourFeatureName).

Commit your changes (git commit -m 'Add some feature').

Push to the branch (git push origin feature/YourFeatureName).

Open a pull request.

License 📄 This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments 🙏 Dataset: UCI Heart Disease Dataset

Flask Documentation: Flask

Scikit-learn Documentation: Scikit-learn

Made with ❤️ by Sumit kapadia

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