Model estimation

Once the feature sets get finalized in our last section, what follows is an estimation of all the parameters of the selected models, for which we adopted the approach of using MLlib on the Zeppeline notebook for this project and R notebooks in the Databricks environment because we need to estimate some regression and time series models.

Similarly to before, for the best modeling, we need to arrange distributed computing, especially for this case with various kinds of services. In other words, we will estimate models to predict the daily volume of each kind of service request, which is for heating, construction-related, noise-related, parking-related, and other service requests.

In order to complete this task of estimating models for various service types, we need to group all the services into a set of service types. However, for this exercise, we just selected 50 top service types and then conducted parallel computing for model estimation for all these 50 services.

For this...