This notebook contains a simple example of analyzing Toronto Fire data using K-means clustering.
It first pulls a the open fire incident data set and runs K-means clustering for different numbers of centroids. It then pulls the fire station location data set, to see how the fire incident clustering maps to actual fire station location.
In an ideal world, the fire stations would map perfectly to the fire incident centriods (assuming that all calls to fire stations were fire incident related).