ml10.py

#importing necessary libraries
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
# Generate sample data
X, y = make_blobs(n_samples=500, centers=4, cluster_std=0.8, random_state=42)
# Create a K-Means clusterer with 4 clusters
kmeans = KMeans(n_clusters=4, random_state=42)
kmeans.fit(X)
# Get cluster labels
labels= kmeans.predict(X)
#Plotting the data with cluster labels
plt.figure(figsize=(8, 6))
plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis')
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=100,
c='red', label='Centroids')
plt.title('K-Means Clustering')
plt.xlabel('X')
plt.ylabel('Y')
plt.legend()
plt.show()