I run a small but fast-growing trucking fleet that hauls construction trailers and modular set-ups throughout the Midwest and Southeast. Up to now we have grown through word of mouth; now I want data to guide our next leap. Your brief is to build an algorithm—or a repeatable analytic workflow—that pinpoints factories that build these units and the leasing companies that place them, highlighting the metro areas and corridors where they cluster most densely. The ultimate goal is simple: surface high-potential prospects so I can start new conversations and win their delivery work. Core expectations • Pull or scrape reliable datasets (industry SIC/NAICS codes, permitting filings, state corporation registries, public fleet/lease records, etc.) • Clean, merge and deduplicate that data, flagging each record as “factory,” “leasing company,” or both where applicable • Score prospects by shipment volume potential and distance to our current lanes in the Midwest/Southeast • Map hot spots—city, county, or ZIP-level—so I can see where outreach trips or targeted ads will hit the biggest pay-off • Package the output as a sortable spreadsheet plus a simple visual dashboard (Tableau, Power BI, or a lightweight Python/Plotly app—your call as long as it’s intuitive) Acceptance criteria 1. A minimum of 200 qualified companies split between factories and leasing firms, each with contact name or generic intake email/phone. 2. Hot-spot map clearly showing at least the top 10 concentration zones. 3. Step-by-step notes or scripts so the search can be rerun quarterly without starting from scratch. I’m open to whichever stack you prefer—Python/pandas, R, or even a low-code tool—so long as the results are accurate, transparent, and something I can maintain. Let me know how you’d approach data sourcing, prospect scoring, and any examples of similar market-mapping work you’ve done.