Web scraping · Location data
Multi-Brand Restaurant Location Data Collection
Independent portfolio projectTwo independent portfolio datasets that turn public restaurant-location information into structured, analysis-ready records.
The problem
Location information spread across public store pages is difficult to compare or analyze until fields such as address, city, state and coordinates are collected into a consistent structure.
What was built
- Reviewed the public location-page structure and defined a repeatable record schema.
- Used Python collection workflows to gather store details and geographic fields.
- Normalized the output with Pandas and organized the results for Excel delivery.
- Kept the published evidence limited to record counts and tools that are documented in the project portfolio.
Verified outcome
The work produced structured restaurant-location datasets suitable for filtering, validation and downstream analysis. No client revenue, lead-generation or time-saving result is claimed because those outcomes were not measured for these portfolio projects.
Evidence used for this case study
- The homepage portfolio documents 3,550+ KFC store records processed.
- The homepage portfolio documents 20,117+ Subway locations processed.
- The documented workflow uses Python, BeautifulSoup, Pandas and Excel.
Evidence note: This is an independent technical portfolio project based on public location information. AI Scraping Lab is not affiliated with, endorsed by or commissioned by KFC or Subway. Brand names identify the public sources represented in the datasets.
Need this kind of system for your business?
We can define the source, delivery format, validation checks and measurable success criteria before implementation begins.