A k-means example in Python

from sklearn.cluster import KMeans
import numpy as np
import matplotlib.pyplot as plt

# Sample data for clustering
# Set the seed for reproducibility
np.random.seed(0)

# Generate 100 random points as a 2D NumPy array
data = np.random.uniform(0, 20, size=(100, 2))

# Number of clusters (k)
k = 2

# Creating a KMeans instance with k clusters
kmeans = KMeans(n_clusters=k)

# Fitting the data to the KMeans algorithm
kmeans.fit(data)

# Getting the centroids and labels for the clusters
centroids = kmeans.cluster_centers_
labels = kmeans.labels_

# Visualizing the clustered data
colors = ["g.", "r.", "c.", "y."]

for i in range(len(data)):
 plt.plot(data[i][0], data[i][1], colors[labels[i]], markersize=10)
 
plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', s=150, linewidths=5, zorder=10)
plt.show()