#### 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
Other Books You May Enjoy

# Implementing equal-frequency discretization

Equal-width discretization is easy to compute. However, if the variables are skewed, then there will be many empty bins or bins with only a few values, while most observations will be allocated to a few intervals. This could result in a loss of information. This problem can be solved by adaptively finding the interval cut-points so that each interval contains a similar fraction of observations.

Equal-frequency discretization divides the values of the variable into intervals that carry the same proportion of observations. The interval width is determined by quantiles. Quantiles are values that divide data into equal portions. For example, the median is a quantile that divides the data into two halves. Quartiles divide the data into 4 portions, and percentiles divide the data into 100 portions. As a result, the intervals will most likely have different widths. The number of intervals is defined by the user.

Equal-frequency discretization...