Revisiting an old post
Last September I wrote a post entitled Is the Government realising its ambition for SMEs on G-Cloud? Six months on, I wanted to revisit and update this article, fold in a second Digital Marketplace framework, and share the R code here. Revisiting an old post also provides an opportunity to see if one can simplify and improve older code. Continue reading “Digital Marketplace. Six months later.”
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”
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””