This is a very basic implementation of the K-Means clustering algorithm (Unsupervised learning) in Machine Learning. K-means is an unsupervised machine learning algorithm used to partition a dataset into a specified number (\(k\)) of clusters. It works by grouping data points into clusters based on their similarity, with each point assigned to the cluster with the nearest mean (centroid). The goal is to minimize the variance within each cluster, making points within a cluster as close as possible, while keeping the clusters well-separated. (Operating Instructions) The 'show labels' slider has 3 options, 1: Dont show labels. 2: Show original data Labels. 3: Show predicted Labels. How to use: 1) Move your mouse to the right of the screen to reveal some buttons. 2) use the buttons in that order: -1: to create a randomized dataset of clusters. You can click multiple times until a desired set is reached. -2: To spawn 'k' mount of centroids (use slider to set k) -3: To make the centroids separate and predict the clusters. (sometimes a different amount of k is needed) 3) You have way more control on those settings inside the project. You can play around with it to figure out how the algorithm works. 4) -new- Press space and click to spawn datapoints on screen There is currently no feedback on how the model is performing. Maybe I'll do it later if I have time. Have fun, bye.
