import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.cluster import KMeans
 
# Load the Iris dataset
iris = datasets.load_iris()
X = iris.data[:, :2]  # we will only use the first two features for visualization purposes
 
# Implementing K-means clustering algorithm
kmeans = KMeans(n_clusters=3, random_state=42)
kmeans.fit(X)
centroids = kmeans.cluster_centers_
labels = kmeans.labels_
 
# Visualizing the clusters
plt.figure(figsize=(8, 6))
 
# Plotting data points
plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', s=50, alpha=0.7, edgecolors='k', label='Data Points')
 
# Plotting centroids
plt.scatter(centroids[:, 0], centroids[:, 1], marker='*', s=200, c='red', label='Centroids')
 
plt.xlabel('Sepal Length')
plt.ylabel('Sepal Width')
plt.title('K-means Clustering')
plt.legend()
plt.grid(True)
plt.show()
# your code goes here

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