Book Image

Python Feature Engineering Cookbook - Second Edition

By : Soledad Galli
Book Image

Python Feature Engineering Cookbook - Second Edition

By: Soledad Galli

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.
Table of Contents (14 chapters)

Discretizing the variable into arbitrary intervals

In various industries, it is common to group variable values into segments that make sense for the business. For example, we might want to group the variable age in intervals representing children, young adults, middle-aged people, and retired people. Alternatively, we might group ratings into bad, good, and excellent. On other occasions, if we know that the variable is in a certain scale, for example, logarithmic, we might want to define the interval cut-points within that scale.

In this recipe, we will discretize a variable into pre-defined user intervals using pandas and Feature-engine.

How to do it...

First, let’s import the necessary Python libraries and get the dataset ready:

  1. Import the required Python libraries and classes:
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    from sklearn.datasets import fetch_california_housing
  2. Let’s load the California housing dataset into...