Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering,

Description: Python Feature Engineering Cookbook by Soledad Galli Python Feature Engineering Cookbook, Second Edition will give you the practice, tools, and techniques to streamline your feature engineering pipelines and simplify and improve the quality of your code. With more than 70 methods to transform or create variables, you will find solutions tailored to different datasets and machine learning models. FORMAT Paperback CONDITION Brand New Publisher Description Create end-to-end, reproducible feature engineering pipelines that can be deployed into production using open-source Python librariesKey FeaturesLearn and implement feature engineering best practicesReinforce your learning with the help of multiple hands-on recipesBuild end-to-end feature engineering pipelines that are performant and reproducibleBook DescriptionFeature 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.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, youll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner.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.What you will learnImpute missing data using various univariate and multivariate methodsEncode categorical variables with one-hot, ordinal, and count encodingHandle highly cardinal categorical variablesTransform, discretize, and scale your variablesCreate variables from date and time with pandas and Feature-engineCombine variables into new featuresExtract features from text as well as from transactional data with FeaturetoolsCreate features from time series data with tsfreshWho this book is forThis 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. Author Biography Soledad Galli is a bestselling data science instructor, author, and open-source Python developer. As the leading instructor at Train in Data, she teaches intermediate and advanced courses in machine learning that have enrolled over 64,000 students worldwide and continue to receive positive reviews. Sole is also the developer and maintainer of the Python open-source library Feature-engine, which provides an extensive array of methods for feature engineering and selection.With extensive experience as a data scientist in finance and insurance sectors, Sole has developed and deployed machine learning models for assessing insurance claims, evaluating credit risk, and preventing fraud. She is a frequent speaker at podcasts, meetups, and webinars, sharing her expertise with the broader data science community. Table of Contents Table of ContentsImputing Missing DataEncoding Categorical VariablesTransforming Numerical VariablesPerforming Variable DiscretizationWorking with OutliersExtracting Features from Date and TimePerforming Feature ScalingCreating New FeaturesExtracting Features from Relational Data with FeaturetoolsCreating Features from Time Series with tsfreshExtracting Features from Text Variables Details ISBN1804611301 Author Soledad Galli Publisher Packt Publishing Limited Year 2022 Edition 2nd ISBN-13 9781804611302 Format Paperback Imprint Packt Publishing Limited Place of Publication Birmingham Country of Publication United Kingdom Pages 386 Publication Date 2022-10-31 AU Release Date 2022-10-31 NZ Release Date 2022-10-31 UK Release Date 2022-10-31 Edition Description 2nd Revised edition DEWEY 005.133 Audience Professional & Vocational Subtitle Over 70 recipes for creating, engineering, and transforming features to build machine learning models Replaces 9781789806311 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:138952374;

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