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📊 What Machine Learning Reveals About Online Course Pricing and Popularity

A 3D regression surface built from a national online-education marketplace, showing how course length, instructor rating and category drive predicted price and enrollment demand.

AI & Machine Learning3DAdvanced60 FPS

A 3D scatter of synthetic marketplace courses sits inside a live regression surface: adjust course length, instructor rating, category and marketing spend to watch the model's predicted price and popularity respond in real time.

🔬 What It Demonstrates

The bending mesh is a stand-in for what a gradient-boosted model like XGBoost actually learns — price and demand are non-linear functions of the input features, not a flat plane, with real interaction effects between duration and rating.

🎮 How to Use

Move the length and rating sliders to slide the glowing query marker across the surface, switch category to see the baseline shift, and toggle between the price surface and the popularity surface to compare what drives each outcome.

💡 Did You Know?

In real marketplace data, the priciest courses are rarely the most popular — price and demand often decouple, which is exactly the kind of pattern tree-based models are good at surfacing that linear regression misses.

⚙ Under the hood

A 3D regression surface built from a national online-education marketplace, showing how course length, instructor rating and category drive predicted price and enrollment demand.

machine learningonline coursespricingregressiondata analysisartificial intelligenceThree.js

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