#### 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.
Preface
Chapter 3: Transforming Numerical Variables
Chapter 4: Performing Variable Discretization
Chapter 5: Working with Outliers
Chapter 6: Extracting Features from Date and Time Variables
Chapter 7: Performing Feature Scaling
Chapter 8: Creating New Features
Chapter 9: Extracting Features from Relational Data with Featuretools
Chapter 10: Creating Features from a Time Series with tsfresh
Chapter 11: Extracting Features from Text Variables
Index
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# Creating periodic features from cyclical variables

Some features are periodic, for example, the hours in a day, the months in a year, and the days in a week. They all start at a certain value, say January, go up to a certain other value, say December, and then start over from the beginning. Some features are numeric, such as the hours, and some can be represented with numbers, such as the months, with values of 1 to 12. Yet, this numeric representation does not capture the periodicity or cyclical nature of the variable. For example, December (12) is closer to January (1) than June (6); however, this relationship is not captured by the numerical representation of the feature. But we could change it if we transformed these variables with sine and cosine, two naturally periodic functions.

Encoding cyclical features with the sine and cosine functions allows linear models to leverage the cyclical nature of features and reduce their modeling error. In this recipe, we will create new features...