Wearlytic is an AI-driven web intelligence platform designed to collect, analyze, and visualize clothing brand data from various e-commerce platforms. It helps businesses track competitor strategies, identify market trends, and optimize product positioning through advanced data analytics.
The project is organized into several key components:
- Flask-based REST API service
- MongoDB database integration for product data storage
- Provides paginated product search and filtering capabilities
- Deployed on Vercel for scalability
- Features:
- Advanced search filtering by description, brand, category
- Price range filtering
- Pagination support
- MongoDB Atlas integration
- Data ingestion component
- Concurrent web scraping capabilities
- Extracts product information from e-commerce platforms
- Features:
- Automated data extraction
- Product detail collection (pricing, availability, reviews)
- Design attribute analysis
- Concurrent request handling
- Interactive dashboards and visualizations
- User-friendly interface for data exploration
- Features:
- Real-time data visualization
- Interactive product filtering
- Trend analysis displays
- Competitive analysis tools
- Data processing and transformation pipeline
- Prepares raw scraped data for analysis
- Features:
- Data cleaning and normalization
- Feature extraction
- Data enrichment
- Format standardization
- Automated Data Collection – Intelligent scraping of product details including pricing, availability, reviews, and design attributes
- AI-Powered Analytics – Advanced analysis of clothing categories, pricing trends, and customer preferences
- Competitive Intelligence – Comprehensive brand comparison based on pricing strategies, product demand, and customer sentiment
- Trend Prediction – Machine learning models to identify emerging fashion trends and forecast product viability
- Interactive Visualization – Dynamic dashboards for data-driven decision making
- Market research for fashion brands and retailers
- Competitive analysis and pricing strategy optimization
- Product development and customer preference analysis
- Trend identification and forecasting
- Inventory and pricing optimization
- Python 3.7 or higher
- Node.js and npm
- MongoDB Atlas account
- Vercel account (for backend deployment)
- Clone the repository
- Set up each component following their respective README files
- Configure environment variables
- Start the services:
- Backend:
cd backend && python app.py - Frontend:
cd frontend && npm start
- Backend:
This project was in prototype phase until now. We are currently building a scalable scraping agent with API exposure capabilities.
The scraping agent is currently being enhanced for better concurrent request handling and data extraction capabilities.
Contributions are welcome! Please read our contributing guidelines and submit pull requests to the appropriate branches.
This project is licensed under the terms specified in the LICENSE file.