Implement AI-powered personalized workout recommendations #71
Description
Description:
Use machine learning to analyze user data and provide personalized workout recommendations based on fitness level, goals, and progress.
Tasks:
Data Collection:
- Collect user fitness data (age, weight, height, goals)
- Track workout performance metrics
- Monitor progress over time
- Gather user feedback on workouts
ML Model Development:
- Design recommendation algorithm
- Implement collaborative filtering
- Add content-based filtering
- Create hybrid recommendation system
- Train model on collected data
Integration:
- Create recommendation API endpoints
- Integrate with frontend recommendation UI
- Add A/B testing for recommendations
- Implement feedback loop for model improvement
Technical Stack:
- Python ML libraries (scikit-learn, TensorFlow/PyTorch)
- Recommendation algorithms
- User behavior analysis
- Real-time recommendation updates
Acceptance Criteria:
- Personalized workout recommendations based on user data
- Recommendations improve over time with user feedback
- A/B testing shows improved user engagement
- Real-time recommendation updates
- Comprehensive testing with diverse user profiles