Overview

Developed a distributed web scraping platform that collects and analyzes product data from over 100 e-commerce websites. The system uses machine learning to adapt to website changes and maintains 95% accuracy in data extraction.

Key Features

• Adaptive Scraping: ML models detect and adapt to website structure changes
• Distributed Architecture: Scales horizontally to handle 1M+ pages daily
• Anti-Detection: Rotating proxies, user agents, and intelligent rate limiting
• Data Quality: Automatic validation and cleansing pipelines

Technical Highlights

Scraping Engine

class IntelligentScraper:
    def __init__(self):
        self.selector_cache = RedisCache()
        self.ml_extractor = MLExtractor()
        
    async def scrape(self, url):
        # Try cached selectors first
        selectors = self.selector_cache.get(url)
        
        if not selectors:
            # Use ML to identify selectors
            selectors = self.ml_extractor.identify_selectors(url)
            self.selector_cache.set(url, selectors)
        
        # Extract data with retry logic
        data = await self.extract_with_retry(url, selectors)
        
        return self.validate_and_clean(data)

Results

• Data Volume: 1M+ product listings collected daily
• Accuracy: 95% extraction accuracy
• Cost Savings: $50K/month vs commercial solutions
• Coverage: 100+ websites across 15 countries