Python Feature Engineering Cookbook: A complete guide to crafting powerful features for your machine learning models

Leverage the power of Python to build real-world feature engineering and machine learning pipelines ready to be deployed to productionKey FeaturesCraft powerful features from tabular, transactional, and time-series dataDevelop efficient and reproducible real-world feature engineering pipelinesOptimize data transformation and save valuable timePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionStreamline data preprocessing and feature engineering in your machine learning project with this third edition of the Python Feature Engineering Cookbook to make your data preparation more efficient.This guide addresses common challenges, such as imputing missing values and encoding categorical variables using practical solutions and open source Python libraries. You’ll learn advanced techniques for transforming numerical variables, discretizing variables, and dealing with outliers. Each chapter offers step-by-step instructions and real-world examples, helping you understand when and how to apply various transformations for well-prepared data.The book explores feature extraction from complex data types such as dates, times, and text. You’ll see how to create new features through mathematical operations and decision trees and use advanced tools like Featuretools and tsfresh to extract features from relational data and time series.By the end, you’ll be ready to build reproducible feature engineering pipelines that can be easily deployed into production, optimizing data preprocessing workflows and enhancing machine learning model performance.What you will learnDiscover multiple methods to impute missing data effectivelyEncode categorical variables while tackling high cardinalityFind out how to properly transform, discretize, and scale your variablesAutomate feature extraction from date and time dataCombine variables strategically to create new and powerful featuresExtract features from transactional data and time seriesLearn methods to extract meaningful features from text dataWho this book is forIf you're a machine learning or data science enthusiast who wants to learn more about feature engineering, data preprocessing, and how to optimize these tasks, this book is for you. If you already know the basics of feature engineering and are looking to learn more advanced methods to craft powerful features, this book will help you. You should have basic knowledge of Python programming and machine learning to get started.Table of ContentsImputing Missing DataEncoding Categorical VariablesTransforming Numerical VariablesPerforming Variable DiscretizationWorking with OutliersExtracting Features from Date and Time VariablesPerforming Feature ScalingCreating New FeaturesExtracting Features from Relational Data with FeaturetoolsCreating Features from a Time Series with tsfreshExtracting Features from Text Variables Read more