Interactive Statistical Learning Notebooks

🎓 Interactive demonstrations for Advanced Data Analytics and Quantitative Data Analysis

🐍 Python 3.12+ | 📊 Marimo | 📈 Plotly | 🧮 WebAssembly

STAT312: Advanced Data Analytics

🔍 k-Nearest Neighbours Classification

Learn the fundamentals of k-NN classification through interactive exploration of decision boundaries, parameter effects, and performance evaluation.

• Real-time decision boundary visualisation
• Interactive parameter adjustment
• Bias-variance trade-off exploration
• Custom prediction points
• Performance comparison plots

🚀 Launch Demo | 📖 Documentation

🧮 Kernel Density Estimation

Explore kernel density estimation techniques through interactive visualisation of density estimates, kernel choices, bandwidth effects, and data distributions.

• Real-time density estimate visualisation
• Interactive bandwidth adjustment
• Multiple kernel function comparison
• Data sampling and generation
• Bias-variance trade-off exploration

🚀 Launch Demo | 📖 Documentation

📈 Non-Parametric Regression

Explore kernel methods and non-parametric regression techniques with side-by-side comparisons of k-NN and Nadaraya-Watson approaches.

• Multiple kernel function comparison
• Bandwidth parameter exploration
• Method comparison visualisation
• Kernel weight visualisation
• Train/test performance analysis

🚀 Launch Demo | 📖 Documentation

STAT420: Quantitative Data Analysis

🌳 Classification and Regression Trees (CART)

Explore decision trees and cost-complexity pruning through interactive tree growth and visualisation.

• Control tree growth with maximum depth parameter
• Interactively prune trees using α parameter
• Visualise complete tree structure with Mermaid diagrams
• Explore non-linear decision boundaries
• Understand bias-variance trade-offs through pruning
• Zoom in/out on tree diagrams for detailed inspection

🚀 Launch Demo | 📖 Documentation