firmai/industry-machine-learning

★ 7,460Jupyter NotebookAudience · dataComplexity · 1/5Setup · easy

Mindmap

mindmap
  root((Industry ML))
    Industries covered
      Healthcare
      Banking and finance
      Agriculture
      Retail and e-commerce
    Content type
      Python notebooks
      Curated links
      Community contributions
    Topics
      Fraud detection
      Drug discovery
      Demand forecasting
    Audience
      Data scientists
      ML practitioners

mindmap root((Industry ML)) Industries covered Healthcare Banking and finance Agriculture Retail and e-commerce Content type Python notebooks Curated links Community contributions Topics Fraud detection Drug discovery Demand forecasting Audience Data scientists ML practitioners

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Things people build with this

USE CASE 1

Find working Python notebook examples for applying machine learning in a specific industry like healthcare or retail.

USE CASE 2

Discover fraud detection code for banking or drug discovery notebooks for biotech as starting points for a project.

USE CASE 3

Use the collection as a reference to understand which ML techniques are being applied in your field.

USE CASE 4

Contribute a new industry notebook or link via pull request to help others find practical ML examples.

Tech stack

PythonJupyter Notebook

Getting it running

Difficulty · easy Time to first run · 30min

This is primarily a curated list, most notebooks link to external repositories that have their own setup requirements.

In plain English

This repository is a curated collection of machine learning and data science code notebooks organized by industry. Instead of focusing on theory, it gathers practical examples of how data science is actually being used in fields like healthcare, banking, agriculture, real estate, education, and roughly a dozen others. Each industry section links to Jupyter Notebooks and libraries written mostly in Python. The collection was originally built in the style of "awesome" lists, which are popular on GitHub as community-maintained directories of resources on a given topic. Here the topic is industry applications of machine learning, and contributors have added notebooks covering things like fraud detection in banking, drug discovery in biotech, demand forecasting in retail, and similar real-world problems. The README contains a large table of contents broken down by sector and sub-sector. The repository is connected to a company called Sov.ai, which works on applying machine learning to financial data and quantitative investing. As of 2024, the repository's maintainers were using it partly as a recruiting channel, seeking PhD-level researchers interested in financial machine learning projects. The README describes Sov.ai's work with quantitative hedge funds and lists sample research directions such as using satellite imagery or GitHub activity data to inform investment decisions. For a non-technical reader, the most useful aspect of this repository is its breadth. If you want to know whether machine learning has been applied to a specific industry, and whether working code examples exist, this list is a practical starting point. The repository accepts contributions via pull requests or through a linked Google Sheet. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1

I am building a fraud detection model for a bank. Which notebooks in firmai/industry-machine-learning cover that problem, and how do I run them?

Prompt 2

Show me how to use a demand forecasting example from this repository to predict retail inventory needs in Python.

Prompt 3

I work in healthcare data. Which machine learning notebooks in this repo are relevant to clinical data or drug discovery?

Prompt 4

How do I contribute a new industry machine learning notebook to this repository via pull request?

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