One of the salient characteristics of text is its complexity. Long descriptions are more likely to contain more information than short descriptions. Texts rich in different, unique words are more likely to be richer in detail than texts that repeat the same words over and over. In the same way, when we speak, we use many short words such as articles and prepositions to build the sentence structure, yet the main concept is often derived by the nouns and adjectives we use, which tend to be longer words. So, as you can see, even without reading the text, we can start inferring how much information the text provides by determining the number of words, the number of unique words, the lexical diversity, and the length of those words. In this recipe, we will learn how to extract these features from a text variable using pandas.
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Python Feature Engineering Cookbook
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Python Feature Engineering Cookbook
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Overview of this book
Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code.
Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains.
By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems.
Table of Contents (13 chapters)
Preface
Foreseeing Variable Problems When Building ML Models
Free Chapter
Imputing Missing Data
Encoding Categorical Variables
Transforming Numerical Variables
Performing Variable Discretization
Working with Outliers
Deriving Features from Dates and Time Variables
Performing Feature Scaling
Applying Mathematical Computations to Features
Creating Features with Transactional and Time Series Data
Extracting Features from Text Variables
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