#### 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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# Using the square root to transform variables

The square root transformation, √x, as well as its variations, the Anscombe transformation, √(x+3/8), and the Freeman-Tukey transformation, √x + √(x+1), are variance stabilizing transformations that transform a variable with a Poisson distribution into one with an approximately standard Gaussian distribution. The square root transformation is a form of power transformation where the exponent is 1/2 and is only defined for positive values.

The Poisson distribution is a probability distribution that indicates the number of times an event is likely to occur. In other words, it is a count distribution. It is right-skewed and its variance equals its mean. Examples of variables that could follow a Poisson distribution are the number of financial items of a customer, such as the number of current accounts or credit cards, the number of passengers in a vehicle, and the number of occupants in a household.

In this...