#### 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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# Transforming Numerical Variables

Statistical methods used in data analysis make certain assumptions about the data. For example, in the general linear model, it is assumed that the values of the dependent variable (the target) are independent, that there is a linear relationship between the target and the independent (predictor) variables, and that the residuals – that is, the difference between the predictions and the real values of the target – are normally distributed and centered at 0. When these assumptions are not met, the resulting probabilistic statements might not be accurate. To correct for failure in the assumptions and thus improve the performance of the models, we can transform variables before the analysis.

Variable transformation consists of replacing the original variable values with a function of that variable. More generally, transforming variables with mathematical functions helps reduce variable skewness, improve the value spread, and sometimes...