Comprehensive Australian Vehicle Data Scraping

I have a list of roughly 1500 URLs—each coming from the same automotive website—that together cover the top 100 makes, models, grades and variants sold in Australia. I need every data point the site makes available for each of those vehicles, from the obvious specs such as year, make, model and variant right through to driveway prices, engine type, transmission, drive configuration, warranty details, fuel-economy figures, in-car technology features, seating layouts and any other attributes exposed on the page.

The end goal is a clean, analysis-ready Excel workbook that lets me run market-wide comparisons, so consistency is critical: headings must be standardised, units normalised and categorical values written the same way across the entire sheet. I am happy for you to use Python, Scrapy, BeautifulSoup, Selenium, AI-assisted extraction—whatever combination you trust—to pull the information, as long as the final file is accurate and complete. Data standardisation is essential.

To keep things efficient I’d like a small sample delivered early so we can confirm structure before you harvest the full set. Once the sample is approved, scrape the remaining URLs, run your data-cleaning pipeline and hand over the finished spreadsheet. If a field is genuinely missing on the source page, leave it blank; otherwise every row should be populated.

Deliverables
• Sample Excel file covering 5–10 vehicles for sign-off
• Final Excel workbook containing all scraped data, one row per variant, with clearly labelled columns and normalised values

That’s the entire scope. If you have questions about edge cases or need extra metadata captured, let me know and we can lock it in before you start the main run.