Python: Real World Machine Learning

Summary

In this chapter, you learned and applied a set of techniques that enable us to effectively build and finesse datasets for machine learning, starting from very little initial data. These powerful techniques enable a data scientist to turn seemingly shallow datasets into opportunities. We demonstrated this power using a set of customer service tweets to create a travel disruption predictor.

In order to take that solution into production, though, we'd need to add some functionality. Removing some locations in the penultimate step was a questionable decision; if this solution is intended to identify journey disruption risk, then removing locations seems like a non-starter! This is particularly true given that we do not have year-round data ...