Real-World Machine Learning

Chapter 6. Example: NYC taxi data

This chapter covers

• Introducing, visualizing, and preparing a real-world dataset about NYC taxi trips
• Building a classification model to predict passenger tipping habits
• Optimizing an ML model by tuning model parameters and engineering features
• Building and optimizing a regression model to predict tip amount
• Using models to gain a deeper understanding of data and the behavior it describes

In the previous five chapters, you learned how to go from raw, messy data to building, validating, and optimizing models by tuning parameters and engineering features that capture the domain knowledge of the problem. Although we’ve used a variety of minor examples throughout these chapters to illustrate the points of ...