Python Feature Engineering Cookbook. (Record no. 16039)
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| control field | 20250710182907.0 |
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| Standard number or code | 9781835883594 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | PACKT |
| Transcribing agency | PACKT |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | en |
| 044 ## - COUNTRY OF PUBLISHING/PRODUCING ENTITY CODE | |
| MARC country code | GB |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Soledad Galli |
| Relator term | author. |
| 245 00 - TITLE STATEMENT | |
| Title | Python Feature Engineering Cookbook. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | GB: |
| Name of publisher, distributor, etc. | Packt, |
| Date of publication, distribution, etc. | 2024-08-30. |
| 263 ## - PROJECTED PUBLICATION DATE | |
| Projected publication date | 2024-08-30 |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | GB: |
| Name of producer, publisher, distributor, manufacturer | Packt, |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 396. |
| 377 ## - ASSOCIATED LANGUAGE | |
| Language code | en |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | <p><b>Leverage the power of Python to build real-world feature engineering and machine learning pipelines ready to be deployed to production</b></p><h4>Key Features</h4><ul><li>Craft powerful features from tabular, transactional, and time-series data</li><li>Develop efficient and reproducible real-world feature engineering pipelines</li><li>Optimize data transformation and save valuable time</li><li>Purchase of the print or Kindle book includes a free PDF eBook</li></ul><h4>Book Description</h4>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.<h4>What you will learn</h4><ul><li>Discover multiple methods to impute missing data effectively</li><li>Encode categorical variables while tackling high cardinality</li><li>Find out how to properly transform, discretize, and scale your variables</li><li>Automate feature extraction from date and time data</li><li>Combine variables strategically to create new and powerful features</li><li>Extract features from transactional data and time series</li><li>Learn methods to extract meaningful features from text data</li></ul><h4>Who this book is for</h4>If 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. |
| 538 ## - SYSTEM DETAILS NOTE | |
| System details note | Data in extended ASCII character set. |
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| System details note | Mode of access: Internet. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Christoph Molnar |
| Relator term | author. |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | PACKT |
| 773 0# - HOST ITEM ENTRY | |
| Title | Python Feature Engineering Cookbook |
| Place, publisher, and date of publication | GB,Packt,2024-08-30 |
| Physical description | 396 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://learning.packt.com/product/476184">https://learning.packt.com/product/476184</a> |
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