A field guide to industry ML notebooks

What it does This repository is a curated index of machine learning and data science projects organized by commercial sector. It links to hundreds of Python notebooks and libraries tackling use cases as varied as restaurant inspection forecasting, hotel dynamic pricing, and genomics sequencing. The catalogue treats machine learning as a utility rather than a sport, focusing on code that solves line-of-business problems instead of chasing benchmark scores.

The interesting bit The taxonomy is the real product. The maintainer slices the field into roughly twenty verticals—Accommodation, Biotech, Legal, Utilities, and so on—then drills down into sub-disciplines like menu recommendation or coal-and-gas forecasting. That structure turns a scattered landscape of GitHub experiments into something resembling an industrial trade directory.

Key highlights

• Covers 20+ verticals including Agriculture, Banking, Healthcare, Real Estate, and Utilities
• Links to concrete projects such as RobotChef for recipe refinement, HotelSpamDetection for fake review detection, and XBRL-AI for accounting anomalies
• Inspired by awesome-machine-learning but focused strictly on applied, industry-specific use cases
• Accepts community contributions via a public Google Sheet and pull requests
• Primarily Python and Jupyter notebooks, with occasional R

Caveats

• Explicitly labeled a “work in progress” with open calls for subject experts to populate and maintain individual industry sections
• Links are crowd-sourced, so freshness and maintenance status depend on ongoing community curation

Verdict Useful if you are a data scientist or ML engineer scouting for domain-specific examples. Avoid if you need a single integrated library; this is strictly a curated index.