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A data-driven airfare transparency platform that benchmarks fair prices and uncovers the role of competition and market dominance.
the analysis of homelessness and it's correlation with government subsidized shelter
“Can we develop a predictive and simulation framework to identify when and where Toronto’s shelter system operates under critical capacity pressure?”
We built a Financial Resilience Score that uses personal‑finance data to reveal how well adults can handle financial shocks, helping institutions spot vulnerability early
We created a numerical metric to evaluate and test financial resilience.
Team 19's datathon project of the Public Service Case.
Worried about the next economic shock? Discover how your financial profile could shape your future — and uncover the hidden risks before they hit.
This project investigates structural determinants of airfare variation in the U.S. domestic airline market.
Domestic U.S. airfare markets and translate findings into actionable insights for travelers, airlines, and policymakers.
Fair Fare uses LightGBM and SHAP to translate structural economic features into plain-English insights, quantifying the dollar-impact of anti-competitive forces on domestic flight routes.
Airline Dataset - How do fares differ between routes that touch highly dominant hub cities versus more competitive markets?
Who is most at risk of financial stress? Using modeling and shock simulation, we identify vulnerable households and design targeted resilience strategies.
Predicting U.S. Airfare Through Market Structure
Toronto shelter occupancy dashboard + predictive ranking tool that flags near-full programs, spots hotspots and seasonality, and supports load balancing recommendations to improve planning.
We investigate shelter occupancy seasonal trends.
A dashboard that uses machine learning with homeless shelter, weather, and immigration data in Toronto to show NGOs how to allocate resources and reduce reliance on municipal bureaucracy.
Predicting Route Price Using Socio-economic & Market Share Data
A capacity-pressure decision framework that turns Toronto Shelter occupancy data(2024-2025) into targeted actions: restore offline beds, prioritize expansions, and test scenarios to boost resilience.
An interactive pricing intelligence platform that evaluates whether domestic air routes are structurally overpriced or underpriced based on competition, demand, and hub dominance. By Team 25.
Train interpretable model (XGBoost GBM) to predict expected flight fares per-mile, use SHAP to explain why fare is inflated. Produce corridor rankings (cost-effectiveness), suggest cheaper corridors.
Targeting highest-strain shelters to improve coordination and reduce overflow
Baseline risk, shock vulnerability, and the economic factors associated with being at financial risk across age demographics.
group 34 project for public service case of datathon 2026
Observing stress in Toronto's Public Shelter systems
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