Book Image

Python Feature Engineering Cookbook

By : Soledad Galli
Book Image

Python Feature Engineering Cookbook

By: Soledad Galli

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)

Deriving Features from Dates and Time Variables

Date and time variables are those that contain information about dates, times, or date and time. In programming, we refer to these variables as datetime variables. Examples of the datetime variables are date of birth, time of the accident, and date of last payment. The datetime variables usually contain a multitude of different labels corresponding to a specific combination of date and time. We do not utilize the datetime variables in their raw format when building machine learning models. Instead, we enrich the dataset dramatically by deriving multiple features from these variables. In this chapter, we will learn how to derive a variety of new features from date and time.

This chapter will cover the following recipes:

  • Extracting date and time parts from a datetime variable
  • Deriving representations...