Hi guys,

Before you is an article guide to open data sets for machine learning. In it, I, for a start, will collect a selection of interesting and fresh (relatively) datasets. And as a bonus, at the end of the article, I will attach useful links on independent search of datasets.

Less words, more data.

A selection of datasets for machine learning:

• Data deaths and battles from the game of thrones — This data set combines three data sources, each based on information from a series of books.
• Global Terrorism Database — Over 180,000 terrorist attacks worldwide, 1970-2017.
• Bitcoin, historical data — Bitcoin data with an interval of 1 minute from selected exchanges, January 2012 — March 2019

Couple of years ago my team (compliance in one of Swiss banks) and I had an interesting task to implement — we had to generate a huge random graph of financial transactions between clients, companies and ATMs. Moreover, we wanted this graph to contain some money-laundering and other financial crime patterns alongside with nodes description such as names, addresses, currencies etc. Obviously, all data should be randomly generated from scratch as long as we could not use any real data for obvious reasons.

As a solution we wrote a generator that I’d love to share with you. This article explains why we needed it and how this generator is working, but if you don’t want to read and want to try it on your own here is the code: https://github.com/MGrin/transactions-graph-generator. I hope that our experience will be helpful to any of you.

Sberbank is the largest bank in Russia and Eastern Europe. Our team in Sbertech teaches Sberbank efficient work with Free & Open Source Software. You can read more about this on Habr (what we exactly do, yet in Russian).

One of the main challenges is to open the mind of managers and engineers for using FOSS (Free & Open Source Software) properly. Because we have a lot of them, we have tried to use GIFs for answer the most common questions.