Python Feature Engineering Cookbook: Over 70 recipes for identifying, selecting and filtering features to build ML models

26 1
Python Feature Engineering Cookbook: Over 70 recipes for identifying, selecting and filtering features to build ML models
January 22, 2020 | ISBN: 9781789806311 | English | 372 pages | True (PDF, EPUB, MOBI) | 25.12 MB
Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries

Key Features

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

Book Description

Feature engineering is invaluable for developing and enriching your machine learning models. In this Python 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 you will learn

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

Who This Book Is For

This book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book.

All comments

    Load more replies

    Join the conversation!

    Login or Register
    to post a comment.

    Advanced Search