Learn Python by Building Data Science Applications

Adding features to our service

At this point, our API has two GET calls and one POST call. The GET call estimates the time it will take for each type of complaint to be closed, based on the historic median for each type of call. However, this approach is obviously very naive—it takes into account neither location, time, nor a number of similar complaints in the queue for the same area. To improve our estimate, let's use an ML model, trained to predict a given complaint type, location, and time. You can find all of the details on model training in the 311model.ipynb notebook. What is important is that the trained model is stored as a Pickle file and expects four features (we collected earlier): type of complaint, latitude, longitude, and time ...