## Inspiration

With the goal of finding ways to help improve Toronto's transit system, we analysed the relationship between TTC bus delays, weather and ridership data. After exploring the datasets we built time series and machine learning models to analyse the causes and predict future delay incidence. From our investigation we came up with actionable insights that could be used to minimise bus delays in Toronto.

What it does

Improves the TTC system

How I built it

With love and python featuring CatBoost, XGBoost, Random Forests, and AutoRegressive Integrated Moving Average!

Challenges I ran into

Finding good data sources and dealing with highly correlated variables

Accomplishments that I'm proud of

Learning more about data models

What I learned

About data models

What's next for Driving Aways Delays

Looking beyond weather and ridership for bus delay causes, as well as subway and street cars

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