Article Image
Article Image

2 weeks into CS489 Machine Learning, I applied what I learned to Calgary Fire Department open data.

I was excited to take what I was learning in my CS489 Machine Learning course at University of Waterloo and apply it to a new data set.

When I walked by the Canada’s Open Data Exchange (CODX) booth at Hack the North, I realized what a perfect opportunity I had found.

Searching through the countless data sets, I found an interesting one from the City of Calgary Fire Department that contained response times by different scenario categories.

Writing this weeks after the hacking was finished, I realize that a naive perceptron algorithm was not the best algorithm to use for this type of regression problem. I’ve also since learned the importance of regularization and massaging the data so a model can be trained effectively on it.

All of that being said, the naive perceptron still predicted at 95% accuracy after 500 iterations on the data set below.

Another great Hack the North, 4th year in a row!

Data Source

Tech Stack

  • Python 3.6
  • Pandas
  • NumPy
  • Jupyter

Check out the submission on Devpost and repo on GitHub.

If this is the type of impact you want a full stack dev to have starting May 2018, let's chat. Email me at or check my resume.

Andrew Paradi

Andrew Paradi

I study computer science at University of Waterloo, built 7 apps in 16 weeks at Atomic, and don't sleep at hackathons.



Andrew Paradi

Full Stack Engineer

Experience Projects Blog About Contact Resume