Coding Challenge: K-means Visualisation and Pixel Manilpulation Using p5.JS/Processing

K-means Clustering is by far the easiest machine learning algorithm (that also happens to be unsupervised and requires practically no maths) to grasp and for beginners it could be a good stepping-stone into ML. As an example to what I had in mind, I created [this](https://delsquared.github.io/K-Means-Clustering-Visualisation/) animation using p5.js that could easily be converted to use some real world dataset rather than random points.

K-means can also be used to create painting-looking art from images (sometimes if it doesn't converge on a solution it also oscillates). I have made some examples links here:
- [building](https://delsquared.github.io/K-Means-Clustering-Visualisation/building.html)
- [clouds](https://delsquared.github.io/K-Means-Clustering-Visualisation/clouds.html)
- [flower](https://delsquared.github.io/K-Means-Clustering-Visualisation/flower.html)
- [kalimba](https://delsquared.github.io/K-Means-Clustering-Visualisation/kalimba.html)
- [lamp](https://delsquared.github.io/K-Means-Clustering-Visualisation/lamp.html)
- [leaf](https://delsquared.github.io/K-Means-Clustering-Visualisation/leaf.html)
- [plant](https://delsquared.github.io/K-Means-Clustering-Visualisation/plant.html)
- [plant2](https://delsquared.github.io/K-Means-Clustering-Visualisation/plant2.html)
- [sea](https://delsquared.github.io/K-Means-Clustering-Visualisation/sea.html)