My current web-scraping script is completely functional in Python but still crawls through its target list one page at a time. I need that changed. The goal is to pinpoint the slow spots, refactor the code, and introduce true parallel execution so each batch of URLs is processed simultaneously without breaking site etiquette or overloading memory. Scope of work • Profile the existing Python code, highlight the performance bottlenecks, and explain why they occur. • Implement efficient concurrency—asyncio, multiprocessing, or concurrent.futures are fine, as long as the final solution reliably spawns parallel jobs and scales on a multi-core machine. • Keep all existing parsing logic intact (BeautifulSoup, requests, Selenium, or Scrapy modules already in place). Any module upgrades must stay compatible with Python 3. • Integrate polite throttling, retries, and graceful error handling so the scraper keeps moving even when individual requests fail or rate limits hit. • Deliver a concise run guide plus a quick benchmark that shows the new parallel version outperforming the original single-threaded run on the same data set.