# Heart Attack Prediction System

## Overview
The **Heart Attack Prediction System** is a web application that helps predict the risk of heart attacks based on user-provided health data. Using machine learning models, it analyzes multiple health factors and generates a risk assessment for the user.

## Features
- **Prediction**: Provides a prediction of heart attack risk based on inputs.
- **Authentication**: Secure user registration and login system with password protection.
- **User Management**: Allows users to manage their accounts securely.
- **User Interface**: Interactive UI for data input and displaying results.

## Technologies Used
- **Frontend**: HTML, CSS, JavaScript, Bootstrap
- **Backend**: Python (Flask framework)
- **Database**: MySQL
- **Machine Learning**: Random Forest Classifier (scikit-learn)
- **Security**: Password hashing with Werkzeug

## Setup Instructions
1. **Clone the repository:**
   ```
   git clone <repository-link>
   ```
2. **Install dependencies:**
   ```
   pip install -r requirements.txt
   ```
3. **Database Setup:**
   - Install MySQL and create a database named `heart_attacker`.
   - Update `SQLALCHEMY_DATABASE_URI` in `app.py` with your MySQL credentials.

4. **Run the application:**
   ```
   python app.py
   ```
5. **Access the application:**
   - Open a browser and go to `http://localhost:5000`

## Usage
- Register or log in to use the system.
- Input the required health data to receive predictions about heart attack risk.

## Contributing
Contributions are welcome! Fork the repository and create a pull request with your improvements.

## License
This project is licensed under the MIT License.


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