Welcome to física ! física is a web application designed to help users track their daily activities, diet, and health metrics to provide personalized insights and recommendations for improving fitness, nutrition, and overall well-being.

• Activity Tracking: Users can log their daily activities, including exercises, workouts, and other physical activities.
• Diet Logging: Users can input their daily dietary intake, including meals, snacks, and beverages.
• Health Metrics: Users can track various health metrics such as weight, body measurements, heart rate, and sleep patterns.
• Machine Learning Models: Utilizes machine learning algorithms to analyze user data and provide personalized insights and recommendations.
• Dashboard: Provides users with a comprehensive dashboard displaying their activity, diet, and health metrics over time.
• Recommendations: Offers personalized recommendations for improving fitness, nutrition, and overall well-being based on the user's data and machine learning analysis.

• Frontend: HTML, CSS, JavaScript, React.js
• Backend: Node.js, Express.js
• Database: MongoDB
• Machine Learning: Python, scikit-learn, TensorFlow
• Deployment: Docker, Heroku
• UI/UX Design: Figma

Check out the design of WellnessTracker on Figma: física Design

1. Clone the repository:

git clone https://github.com/your-username/física.git

2. Navigate to the project directory:

cd health_tracker

3. Install dependencies:

npm install

4. Start the application:

npm start

1. Register for a new account or log in with existing credentials.
2. Input your daily activities, diet, and health metrics using the provided forms.
3. Explore personalized insights and recommendations generated by machine learning models.
4. Monitor your progress over time using the dashboard.

física can be easily customized for different purposes:

• Additional Features: Add new features such as meditation tracking, water intake logging, or mood tracking to tailor the application to your specific needs.
• Integration with Wearable Devices: Incorporate APIs for popular wearable devices such as Fitbit or Apple Watch to automatically sync activity and health data.
• Custom Machine Learning Models: Train and deploy custom machine learning models tailored to your specific data and requirements.
• Multi-Language Support: Implement multi-language support to make the application accessible to users from different regions.
• White-labeling: Customize the branding, colors, and design elements to align with your organization's branding guidelines.

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature: git checkout -b feature-name.
3. Commit your changes: git commit -am 'Add new feature'.
4. Push to the branch: git push origin feature-name.
5. Submit a pull request.

This project is licensed under the ... License - see the LICENSE file for details.

If you found an issue or would like to submit an improvement to this project, please submit an issue using the issues tab above. If you would like to submit a PR with a fux, reference the issue you created!