I have a master list of roughly 5,000 product sub-categories and want to build a fast, reliable Python scraper that can collect 50 B2B supplier records per city for about 3,000 cities worldwide. Scope • Data sources: the script must pull from Google Places, leading global B2B websites, and relevant local business directories. • Geography: priority regions are North America, Europe, and Asia, but the logic should be flexible enough to run on any continent. • Supplier types: focus on Manufacturers, Distributors, and Wholesalers. Required output per supplier 1. Company name 2. Website URL (if any) 3. Full street address 4. Phone and mobile numbers 5. Contact email 6. Key people or decision-makers 7. Link to catalog PDFs (when available) 8. Credentials or certifications noted on the source page 9. Public rating or review score (if provided by the source) Technical expectations • Written in Python 3.x using requests/BeautifulSoup, Selenium, or the Google Places API—whichever combination you believe will reach quota limits fastest while remaining stable. • Clean, well-commented code that I can run from the command line with a single configuration file for cities and categories. • Output as CSV or TSV with UTF-8 encoding; one row per supplier, duplicate suppression built in. • Respect site terms and reasonable rate limiting; implement retry logic and simple proxy rotation. Timeline I need an initial working script plus a sample data file for ten cities within five days of project start. We will test against that sample, then you can refine scraping rules or add secondary sources as needed. All code and documentation must be delivered before final hand-off.