## 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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