An intelligent fashion recommendation engine that delivers personalized product suggestions using computer vision and machine learning.
- 🔍 Hybrid recommendation engine using collaborative filtering & visual search
- 🛍️ Real-time personalization based on user preferences & behavior
- 📸 Image-based search – Upload a reference photo to find similar styles
- 📊 A/B testing framework for optimizing recommendations
- 🔒 Privacy-focused design with built-in bias detection
├── recommendation_engine/
│ ├── collaborative_filtering/
│ ├── content_based/
│ └── visual_search/
├── api/
├── ml_models/
├── data_processing/
└── deployment/
- Backend: 🚀 Python (FastAPI), PostgreSQL, Redis
- ML Framework: 🤖 PyTorch, OpenCV
- Deployment: 📦 Docker, Kubernetes
- Monitoring: 📈 Prometheus, Grafana
Ensure you have the following installed:
✅ python >= 3.8
✅ docker >= 20.10
✅ kubectl >= 1.20
1️⃣ Clone the repository & setup environment:
git clone https://github.com/your-username/fashion-recommendation-system.git
cd fashion-recommendation-system
pip install -r requirements.txt
2️⃣ Start the services:
docker-compose up -d
python app.py
3️⃣ Access the API at: http://localhost:8000
🚀
pytest tests/
- 🍴 Fork the repository
- 🌿 Create a feature branch
- 📝 Submit a PR with tests & documentation
- 🎯 98% recommendation accuracy
- ⚡ 150ms average response time
- 📡 10K requests/second throughput
LICENSE
file for details.
For licensing inquiries & support: 📧 [email protected]