implementing K-means algorithm in Python

In this this exercise, you will implement the K-means algorithm. You will experiment with an example 2D dataset that will help you gain an intuition of how the K-means algorithm works. The K-means algorithm is a method to automatically cluster similar data examples together. Concretely, you are given a training set 〖{x〗^((1)),…,x^((m) )} (where x^((i))∈R^n), and want to group the data into a few cohesive “clusters”. The intuition behind K-means is an iterative procedure that starts by guessing the initial centroids, and then refines this guess by repeatedly assigning examples to their closest centroids and then recomputing the centroids based on the assignments.