Yes, I can help you build a used car valuation product using data from sources like Autotrader, Kelley Blue Book (KBB), Cars.com, CarGurus, etc.

Here’s how we can approach it:

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Goal:

Create a web-based or API-based product that provides estimated value for used cars based on real-time market data.

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Core Features:
1. User Input:
• Make, model, year
• Mileage
• Trim, condition
• Zip code or location
2. Data Collection (Scraping/API):
• Scrape real-time listings from Autotrader or other sites (unless APIs are available)
• Clean and aggregate data
3. Valuation Engine:
• Use regression or machine learning models to estimate value
• Optionally compare to official sources (KBB, NADA, Edmunds)
4. Interface:
• Simple web UI (Bubble, Webflow, or code-based)
• Or an API for integration into other tools
5. Optional Enhancements:
• Price trends over time
• Sellability score
• Alerts for deals on similar cars

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Tech Stack (Options):
• Frontend/UI: Bubble, React, Next.js
• Backend/API: Python (FastAPI), Node.js
• Scraping: BeautifulSoup, Selenium, Playwright (or Scrapy)
• ML/Valuation: Scikit-learn or OpenAI fine-tuned model
• Database: PostgreSQL, Firebase, or MongoDB

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Important Notes:
• Legal/Compliance: Web scraping from sites like Autotrader can violate terms of service. It’s better to use official APIs if available (e.g., KBB API, Carfax API).
• Rate Limits: If scraping, you’ll need rotating proxies and anti-bot strategies.

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Would you like to:
1. Build a quick MVP using scraping?
2. Use legitimate API access (if you have or want to apply for it)?
3. Build just the valuation engine and UI without scraping?

Let me know your preferred direction and budget/tech preference, and I’ll map out a full plan or even build you a starter prototype.