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Download this book in **EPUB** and **PDF** formats

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- Craft powerful features from tabular, transactional, and time-series data
- Develop efficient and reproducible real-world feature engineering pipelines
- Optimize data transformation and save valuable time
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Streamline 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.

- Discover multiple methods to impute missing data effectively
- Encode categorical variables while tackling high cardinality
- Find out how to properly transform, discretize, and scale your variables
- Automate feature extraction from date and time data
- Combine variables strategically to create new and powerful features
- Extract features from transactional data and time series
- Learn methods to extract meaningful features from text data

Download this book in **EPUB** and **PDF** formats

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Publication date :
Aug 30, 2024

Length
396 pages

Edition :
3rd Edition

Language :
English

ISBN-13 :
9781835883587

Category :

Languages :

Concepts :

Preface

1. Chapter 1: Imputing Missing Data

2. Chapter 2: Encoding Categorical Variables

3. Chapter 3: Transforming Numerical Variables

4. Chapter 4: Performing Variable Discretization

5. Chapter 5: Working with Outliers

6. Chapter 6: Extracting Features from Date and Time Variables

7. Chapter 7: Performing Feature Scaling

8. Chapter 8: Creating New Features

9. Chapter 9: Extracting Features from Relational Data with Featuretools

10. Chapter 10: Creating Features from a Time Series with tsfresh

11. Chapter 11: Extracting Features from Text Variables

12. Index

13. Other Books You May Enjoy

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