Scikit-Learner 📈

A web-based machine learning application for training and comparing regression and classification models. This runs scikit-learn directly in the user's browser via Pyodide, so the whole app deploys as a static website.

Features

• 27 Regression Models across 6 categories
• 22 Classification Models across 6 categories
• Interactive Plotly visualizations — scatter, residuals, predicted vs actual, ROC, confusion matrix, comparison bar chart
• Cross-Validation (3 / 5 / 10 folds)
• Sample Datasets — Iris, Wine, Breast Cancer, Digits (classification); Diabetes, Boston-synthetic, Airfoil, Synthetic (regression)
• Model Export — joblib bytes, single-file or zipped bundle

How it works

┌───────────────────────────────────────────────────────────┐
│  Browser                                                  │
│  ┌─────────────────────────────────────────────────────┐  │
│  │  index.html + Bootstrap + Plotly                    │  │
│  │  ↓ pyCall('train', [...])                           │  │
│  │  ┌────────────────────────────────────────────────┐ │  │
│  │  │  pyodide-bridge.js                             │ │  │
│  │  │  • loads Pyodide from JSDelivr CDN             │ │  │
│  │  │  • installs scikit-learn / pandas / numpy /    │ │  │
│  │  │    scipy / joblib                              │ │  │
│  │  │  • runs frontend/py/learner.py inside Pyodide  │ │  │
│  │  │  • thin pyCall / pyCallBinary wrappers         │ │  │
│  │  └────────────────────────────────────────────────┘ │  │
│  └─────────────────────────────────────────────────────┘  │
└───────────────────────────────────────────────────────────┘
                  (no network calls after first load)

First load: ~10 s (downloads Pyodide runtime + sklearn wheel, ~15 MB total). Subsequent loads: ~1 s thanks to browser cache.

Running locally

This is a 100% static site — no Python virtualenv, no Node toolchain, no backend to start. Any static file server will do; the snippet below uses Python's stdlib server only because it's universally available.

python3 -m http.server -d frontend 8080
open http://localhost:8080/

Edit any file under frontend/ and reload the browser.

If you change frontend/py/learner.py, the browser fetches it fresh on reload — but Pyodide doesn't pick up the change until the module is re-imported. Hard-reload (Cmd-Shift-R / Ctrl-F5) or open a new tab.

Deploy

Upload frontend/ to any static host (Netlify, GitHub Pages, S3, …).

Testing

A Playwright end-to-end spec covers Pyodide bootstrap, sample loading, training, predictions, export, and the UI scatter-plot render — 8 assertions, runs against either a local python -m http.server -d frontend or the public URL.

Caveats (WASM)

• Pyodide initial load adds ~10 s and ~15 MB of one-time download. Loading overlay covers it.
• CSV upload capped at 20 MB (Pyodide's WASM heap).
• The airfoil dataset is bundled as frontend/data/airfoil.csv because Pyodide can't reach fetch_openml from inside the browser.
• Boston-housing uses the synthetic generator (real Boston was removed from sklearn ≥1.2).

License

BSD