K-means Clustering Visualizer

Explore unsupervised learning through interactive K-means clustering

from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=k, random_state=42)
labels = kmeans.fit_predict(X)
# Discover hidden patterns in data

Study Mode: Analyze student performance data and see how K-means groups students by academic achievement.

Dataset Type:

Number of Clusters (K):

Number of Points:

Click on the chart to add data points. Each click adds a new point that will be included in the clustering.

Points added: 0

Ready to generate data

Generate data using the controls on the left to see K-means clustering in action.