Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering, and transforming features to build machine learning models

Soledad Galli

Digital

Available

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.

What will you learn from this book

  • Simplify your feature engineering pipelines with powerful Python packages
  • Get to grips with imputing missing values
  • Encode categorical variables with a wide set of techniques
  • Extract insights from text quickly and effortlessly
  • Develop features from transactional data and time series data
  • Derive new features by combining existing variables
  • Understand how to transform, discretize, and scale your variables
  • Create informative variables from date and time
Language English
ISBN-10 9781789806311
ISBN-13 9781789806311
No of pages 372
Book Publisher Packt Publishing
Published Date 22 Jan 2020

About Author

Author : Soledad Galli

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  • Discover solutions for feature generation, feature extraction, and feature selection
  • Uncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasets
  • Implement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy libraries

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