Tech

Day 16 of 100 Days of AI

Michael Tefula

Michael Tefula

— 1 min read

K-Means Clustering Continued…

I went through a lab exercise today on k-means. I’ve put the code on my Github page here.

I remain in awe that you can fit a model with a few lines of code. A snippet is provided below. In this example, a k-means algorithm is run on data assigned to the variable ‘X’. How cool is that?

Here are some cool charts from the code on Github.

Key takeaways:

  • K-means is an unsupervised machine learning technique to cluster data into a ‘k’ number of groups.
  • Example use cases include customer segmentation, fraud detection, and grouping biological markers.
  • It’s useful where the data isn’t labelled, and you want to explore and find patterns that might not be immediately obvious.

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