Month: November 2016

Homebrew addition clustering

There are currently well over 100,000 votes on What to Brew, meaning it’s a treasure trove of data. But data is only as useful as its analysis. This article looks at my work to find groupings of similar homebrew additions based on how well they work with each style. I decided to use k-means clustering┬áto […]