Python Feature Engineering Cookbook
內容描述
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 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.
目錄大綱
Foreseeing Variable Problems When Building ML Models
Imputing Missing Data
Encoding Categorical Variables
Transforming Numerical Variables
Performing Variable Discretisation
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
作者介紹
Soledad Galli is a lead data scientist with more than 10 years of experience in world-class academic institutions and renowned businesses. She has researched, developed, and put into production machine learning models for insurance claims, credit risk assessment, and fraud prevention. Soledad received a Data Science Leaders' award in 2018 and was named one of LinkedIn's voices in data science and analytics in 2019. She is passionate about enabling people to step into and excel in data science, which is why she mentors data scientists and speaks at data science meetings regularly. She also teaches online courses on machine learning in a prestigious Massive Open Online Course platform, which have reached more than 10,000 students worldwide.
