Book Image

Python Feature Engineering Cookbook - Second Edition

By : Soledad Galli
Book Image

Python Feature Engineering Cookbook - Second Edition

By: Soledad Galli

Overview of this book

Feature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes. This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you'll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner. By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production.
Table of Contents (14 chapters)

Implementing maximum absolute scaling

Maximum absolute scaling scales the data to its maximum value; that is, it divides every observation by the maximum value of the variable:

As a result, the maximum value of each feature will be 1.0. Note that maximum absolute scaling does not center the data. It was specifically designed for scaling sparse data. In this recipe, we will implement maximum absolute scaling with scikit-learn.


Scikit-learn recommends using this transformer on data that is centered at 0 or on sparse data.

Getting ready

Maximum absolute scaling was specifically designed to scale sparse data. Thus, we will use a bag-of-words dataset that contains sparse variables for the recipe. In this dataset, the variables are words, the observations are documents, and the values are the number of times each word appears in the document. Most entries in the data are 0.

For guidelines on how to download, prepare, and store the dataset, please...