K-Means is a powerful unsupervised machine learning algorithm used to partition a dataset into a pre-determined number of distinct, non-overlapping clusters. It works by iteratively assigning data points to the nearest cluster "centroid" and then updating the centroid's position based on the mean of the assigned points. This guide breaks down the geometric intuition behind K-Means, explores its core assumptions and limitations, and introduces important variations you should k