One aspect of a 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 previous 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.
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.
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Current Title:
Python Feature Engineering Cookbook
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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