Book Image

Feature Engineering Made Easy

By : Sinan Ozdemir, Divya Susarla
Book Image

Feature Engineering Made Easy

By: Sinan Ozdemir, Divya Susarla

Overview of this book

Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective. You will start with understanding your data—often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data. By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization.
Table of Contents (14 chapters)
Title Page
Copyright and Credits
Packt Upsell


Feature improvement is about recognizing areas of issue and improvement in our data and figuring out which cleaning methods will be the most effective. Our main takeaway should be to look at data with the eyes of a data scientist. Instead of immediately dropping rows/columns with problems, we should think about the best ways of fixing these problems. More often than not, our machine learning performance will thank us in the end.

This chapter contains several ways of dealing with issues with our quantitative columns. The next chapter will deal with the imputing of categorical columns, as well as how to introduce brand new features into the mix from existing features. We will be working with scikit-learn pipelines with a mix of numerical and categorical columns to really expand the types of data we can work with.