#### 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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# Performing Box-Cox transformation

The Box-Cox transformation is a generalization of the power family of transformations and is defined as follows:

Here, y is the variable and λ is the transformation parameter. It includes important special cases of transformations, including untransformed (λ = 1), the logarithm (λ = 0), the reciprocal (λ = - 1), the square root (when λ = 0.5, it applies a scaled and shifted version of the square root function) and the cube root.

In the Box-Cox transformation, several values of λ are evaluated using the maximum likelihood, and the λ parameter that returns the best transformation is selected.

In this recipe, we will perform Box-Cox transformation using scikit-learn and Feature-engine.

Note

The Box-Cox transformation can only be used on positive variables. If your variables have negative values, try the Yeo-Johnson transformation, which is described in the next recipe, Performing Yeo-Johnson...