Problem: Finding underpriced items to resell means refreshing Craigslist by hand and guessing what each one is worth. Solution: Built a Python tracker that scans Craigslist for-sale searches across the SF Bay Area in phones, computers, electronics, tools, bikes, and video games, then matches each title to a catalog of more than 150 models. Resale value comes from average completed sales on Swappa, adjusted for storage size, with discounts for locked or damaged listings, and the profit estimate subtracts selling friction. The matcher skips accessories, bundles, frame-only bikes, and too-old models, and for bikes it reads the full listing description to find the model and year. Results appear in a local web report with ask, resale value, and profit, ranked by margin, with a Refresh button that triggers a new scan. Outcome: A personal deal-finding tool that turns raw classifieds into a ranked list of flips above a set profit threshold, with the matching rules and resale guesses kept in an editable config file.
Python, Web scraping, HTML, JavaScript, JSON, Swappa pricing data
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