Python Feature Engineering Cookbook. (Record no. 15144)
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| control field | 20250710181505.0 |
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| Standard number or code | 9781804615393 |
| 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. | 2022-10-31. |
| 263 ## - PROJECTED PUBLICATION DATE | |
| Projected publication date | 2022-10-31 |
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| Place of production, publication, distribution, manufacture | GB: |
| Name of producer, publisher, distributor, manufacturer | Packt, |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 386. |
| 377 ## - ASSOCIATED LANGUAGE | |
| Language code | en |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | <p><b>Create end-to-end, reproducible feature engineering pipelines that can be deployed into production using open-source Python libraries</b></p><h4>Key Features</h4><ul><li>Learn and implement feature engineering best practices</li><li>Reinforce your learning with the help of multiple hands-on recipes</li><li>Build end-to-end feature engineering pipelines that are performant and reproducible</li></ul><h4>Book Description</h4>Feature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes.<br/>This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you'll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner.<br/>By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production.<h4>What you will learn</h4><ul><li>Impute missing data using various univariate and multivariate methods</li><li>Encode categorical variables with one-hot, ordinal, and count encoding</li><li>Handle highly cardinal categorical variables</li><li>Transform, discretize, and scale your variables</li><li>Create variables from date and time with pandas and Feature-engine</li><li>Combine variables into new features</li><li>Extract features from text as well as from transactional data with Featuretools</li><li>Create features from time series data with tsfresh</li></ul><h4>Who this book is for</h4>This book is for machine learning and data science students and professionals, as well as software engineers working on machine learning model deployment, who want to learn more about how to transform their data and create new features to train machine learning models in a better way. |
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| System details note | Data in extended ASCII character set. |
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| System details note | Mode of access: Internet. |
| 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,2022-10-31 |
| Physical description | 386 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://learning.packt.com/product/426088">https://learning.packt.com/product/426088</a> |
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