ledmaster

ledmaster / ml-mania-2026

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An experiment in turning years of machine learning experience into a research loop that could run on its own

25
1
100% credibility
Found Mar 21, 2026 at 25 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Jupyter Notebook
AI Summary

This GitHub repository shares an AI-driven experiment automating machine learning research to predict NCAA March Madness 2026 tournament outcomes using historical data and betting market overlays.

How It Works

1
🏀 Discover March Madness predictions

You find this fun project on GitHub that uses smart AI to predict NCAA basketball tournament winners.

2
📖 Read the exciting story

Dive into the readme to learn how an expert teamed up with AI to automatically improve predictions over many experiments.

3
🤖 Watch AI invent better ideas

Check the journal to see the AI agent trying features like block rates and market odds, picking winners step by step.

4
⚙️ Run your own predictions

Use simple scripts to create a full set of game probabilities for the 2026 tournament.

5
Tweak with real-time odds
✅
Skip overlays

Stick with pure model predictions.

💰
Add market wisdom

Pull current odds to fine-tune a few key games.

🏆 Get your winning bracket

Enjoy ready-to-use predictions that beat tough validation tests across past tournaments!

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AI-Generated Review

What is ml-mania-2026?

This Jupyter Notebook repo runs a semi-autonomous research loop for Kaggle's March Machine Learning Mania 2026, predicting NCAA men's and women's tournament outcomes from historical basketball data. Users get prompts and scripts that let LLMs like Codex iterate on features, train LightGBM models, blend predictions with Polymarket odds, and generate Stage 2 submissions—all while enforcing strict walk-forward validation on 2021-2025 folds. It's a bike turning experiment in distilling expert ML into self-sustaining LLM agents.

Why is it gaining traction?

Unlike static Kaggle notebooks, it delivers a persistent journal for experiment tracking, evolved prompts that prevent LLM drift, and Polymarket overlays for live edges—handling title futures via dynamic programming over brackets. Devs love the design of experiment github style: autonomous feature families like block rates auto-test against baselines, with Brier scores logged per season.

Who should use this?

Kaggle competitors blending models for noisy tournaments like 2026 brackets, ML engineers prototyping langchain experimental github agents for time-series, or researchers running turing experiments on sports predictions with single cell experiment github rigor.

Verdict

Grab the prompts and journal template for your next chime experiment github—solid proof-of-concept for agentic workflows despite 25 stars and 1.0% credibility score. Maturity lags (light tests, comp-specific), but ports easily to qiskit experiment github or taper turning experiment setups.

(198 words)

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