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.