step we need to specify the desired number of subgroups step fix the number of clusters and randomly assign each data point to cluster or in other words we need to classify our data based on the number of clusters in this stepcluster centroids should be computed as this is an iterative algorithmwe need to update the locations of centroids with every iteration until we find the global optima or in other words the centroids reach at their optimal locations the following code will help in implementing -means clustering algorithm in python we are going to use the scikit-learn module let us import the necessary packagesimport matplotlib pyplot as plt import seaborn as snssns set(import numpy as np from sklearn cluster import kmeans the following line of code will help in generating the two-dimensional datasetcontaining four blobsby using make_blob from the sklearn dataset package from sklearn datasets samples_generator import make_blobs xy_true make_blobs(n_samples= centers= cluster_std= random_state= we can visualize the dataset by using the following codeplt scatter( [: ] [: ] = )plt show(