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)

Estimating text complexity by counting sentences

One aspect of a piece of text we can capture in features is its complexity. Usually, longer descriptions that contain multiple sentences spread over several paragraphs tend to provide more information than descriptions with very few sentences. Therefore, capturing the number of sentences may provide some insight into the amount of information provided by the text. This process is called sentence tokenization. Tokenization is the process of splitting a string into a list of pieces or tokens. In the Counting characters, words, and vocabulary recipe, we did word tokenization – that is, we divided the string into words. In this recipe, we will divide the string into sentences, and then we will count them. We will use the NLTK Python library, which provides this functionality.

Getting ready

In this recipe, we will use the NLTK Python library. For guidelines on how to install NLTK, check out the Technical requirements section...