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📊 Segmenting an Online Course Market

A live 3D k-means clustering of synthetic online-course listings by price, duration, rating and popularity, revealing distinct buyer-segment niches.

AI & Machine Learning3DAdvanced60 FPS

A synthetic marketplace of online-course listings is plotted in 3D by price, duration/popularity and rating, then split into buyer segments live with the k-means clustering algorithm.

🔬 What It Demonstrates

K-means repeatedly assigns each course to its nearest cluster centre and recomputes the centre as the mean of its members, gradually converging on natural buyer niches such as budget crash-courses, premium certifications, and free intro tasters.

🎮 How to Use

Choose how many clusters (k) to search for and how many listings to sample, switch the vertical axis between duration and popularity, and watch inertia drop as the algorithm converges. Resample for a fresh marketplace.

💡 Did You Know?

The same clustering approach that groups course listings here is used by real education marketplaces to decide pricing tiers, bundle offers, and ad targeting for each buyer segment.

⚙ Under the hood

A live 3D k-means clustering of synthetic online-course listings by price, duration, rating and popularity, revealing distinct buyer-segment niches.

Machine LearningClusteringData AnalysisOnline CoursesSegmentationK-MeansThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install