000			03422nam a2200277uu 4500
005			20250710181507.0
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024	8	0	_a9781835883594
040			_aPACKT _cPACKT
041			_aen
044			_aGB
100	0		_aSoledad Galli _eauthor.
700	0		_aChristoph Molnar _eauthor.
710	2		_aPACKT
773	0		_tPython Feature Engineering Cookbook _dGB,Packt,2024-08-30 _h396
245	0	0	_aPython Feature Engineering Cookbook.
300			_a396.
377			_aen
260			_aGB: _bPackt, _c2024-08-30.
263			_a2024-08-30
264		1	_aGB: _bPackt,
520			_a<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			_aData in extended ASCII character set.
538			_aMode of access: Internet.
856	4	0	_uhttps://learning.packt.com/product/476184
999			_c15217 _d15217