Criminal goings-on in a random forest

Criminal goings-on in a random forest

Supervised machine learning

In the “cluster of six”, we used unsupervised machine learning, to reveal hidden structure in unlabelled data, and analyse the voting patterns of Labour Members of Parliament. In this blog post, we’ll use supervised machine learning to see how well we can predict crime in London. Perhaps not specific crimes. But we can use recorded crime summary data at London borough-level (non-personal aggregated data licensed under the Open Government Licence), with some degree of accuracy, to predict crime counts.

Along the way, we’ll see the pay-off from an exploration of multiple models.

Continue reading “Criminal goings-on in a random forest”

The plots thicken

Every story needs a good plot

One could think of data science as “art, grounded in facts”. It tells a story through visualisation. Both story and visualisation rely on a good plot. And an abundance of those has evolved over time. Many have their own dedicated Wikipedia page!

Which generate the most interest? How is the interest in each trending over time? Try this app to find out. Continue reading “The plots thicken”

The “cluster of six”

Unsupervised machine learning

Unsupervised machine learning

Hansard reports what’s said in the UK Parliament, sets out details of divisions, and records decisions taken during a sitting. The hansard R package provides functions to import its data.

Using the Hansard API (Application Programming Interface), we’ll apply unsupervised machine learning to analyze the voting patterns of 219 Labour Members of Parliament (MPs). We’ll consider all divisions (results of the votes) in the UK House of Commons since the 2017 general election. Continue reading “The “cluster of six””