#### 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 spline features

Linear models expect a linear relationship between the predictor variables and the target. However, we can use linear models to model non-linear effects if we first transform the features. In the Performing polynomial expansion recipe, we saw how we can unmask linear patterns by creating features with polynomial functions. In this recipe, we will discuss the use of splines.

Splines are used to mathematically reproduce flexible shapes. They consist of piecewise low-degree polynomial functions. To create splines, we must place knots at several values of x within its value range. These knots indicate where the pieces of the function join together. Then, we fit low-degree polynomials to the data between two consecutive knots.

There are several types of splines, such as smoothing splines, regression splines, and B-splines. scikit-learn supports the use of B-splines to create features. The procedure to fit and therefore return the spline values for a certain...