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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Sports sponsorship intelligence platform for World Cup match data, real-source text signals, ROI prediction, uncertainty analysis, and scenario recommendations.
| Date | Stars |
|---|---|
| 2026-07-24 | 358 |
| 2026-07-25 | 358 |
| 2026-07-28 | 358 |
| 2026-07-30 | 358 |
| 2026-08-06 | 358 |
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# WorldCupROI **AI Sports Sponsorship Intelligence Platform** WorldCupROI blends match performance, media attention, fan behavior, sponsor investment, scenario simulation, and uncertainty risk into one sponsor ROI decision platform. The goal is not only to predict football results, but to help answer: **which sponsorship strategy should a brand choose, under what risk, and why?** [](https://github.com/2417467487-hub/WorldCupROI/actions/workflows/ci.yml)       | Link | Target | |---|---| | Website | [Open WorldCupROI in browser](https://2417467487-hub.github.io/WorldCupROI/) | | Live Demo | `make dashboard` | | Static Dashboard | [dashboard/panel_dashboard.html](dashboard/panel_dashboard.html) | | Executive Summary | [reports/executive_summary.pdf](reports/executive_summary.pdf) | | Business Report | [reports/business_insights.md](reports/business_insights.md) | | Data Card | [reports/data_card.md](reports/data_card.md) | | Model Card | [reports/model_card.md](reports/model_card.md) | | Deployment Guide | [docs/deployment.md](docs/deployment.md) | ### Platform Hero Overview  **Core result snapshot** | Area | Current value | |---|---:| | Platform health score | 100 / 100 | | Match accuracy | 0.5566 | | Match log loss | 0.9780 | | Sponsor ROI MAE | 0.1177 | | Sponsor ROI R2 | 0.8838 | | Match conformal coverage | 0.9021 | | ROI interval coverage | 0.8814 | | Average Monte Carlo std | 0.1320 | ## 10-Second Overview WorldCupROI is a reproducible sports sponsorship analytics project with four layers: | Layer | What it does | Business value | |---|---|---| | Data intelligence | Separates real historical data, real text data, and proxy/mock commercial data | Makes data boundaries visible before decisions | | ML modeling | Trains match outcome and sponsor ROI models with validation outputs | Converts sports and attention signals into measurable ROI forecasts | | Risk and explainability | Adds SHAP-style drivers, conformal intervals, Monte Carlo risk, and scenario lift | Turns point estimates into defensible decisions | | Decision intelligence | Adds optimization, causal evidence, counterfactuals, tail risk, temporal behavior, and graph learning | Moves from ROI prediction to sponsor portfolio decisions | | Product dashboard | Discover -> Explain -> Predict -> Simulate -> Recommend | Makes the work usable by analysts and business reviewers | ## Results Showcase ### Model Performance Comparison  **What it shows:** Compares trained baseline and benchmark models on primary evaluation metrics. **Why it matters:** It shows whether the current model choice is a stable baseline or only a placeholder. **Business takeaway:** Use the benchmark spread to decide which model family deserves production tuning first. | Task | Model | Metric | Value | |---|---|---|---:| | Match outcome | Centroid classifier | Accuracy | 0.5566 | | Match outcome | Centroid classifier | Log loss | 0.9780 | | Sponsor ROI | Ridge regression | MAE | 0.1177 | | Sponsor ROI | Ridge regression | R2 | 0.8838 | ### ROI Feature Importance / SHAP  **What it shows:** Ranks the strongest sponsor ROI drivers using SHAP-style feature contribution scores. **Why it ma
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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