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    Python Feature Engineering Cookbook

    More Information
    Learn
    • Simplify you鸿运彩软件下载r feature engineering pipelines with powerful Python packages
    • Get to grips with imputing missing values
    • Encode categorical variables with a wide set of techniques
    • Extract insights from text quickly and effortlessly
    • Develop features from transactional data and time series data
    • Derive new features by combining existing variables
    • Understand how to transform, discretize, and scale you鸿运彩软件下载r variables
    • Create informative variables from date and time
    About

    Feature engineering is invaluable for developing and enriching you鸿运彩软件下载r machine learning models. In this cookbook, you鸿运彩软件下载 will work with the best tools to streamline you鸿运彩软件下载r feature engineering pipelines and techniques and simplify and improve the quality of you鸿运彩软件下载r code.

    Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you鸿运彩软件下载’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you鸿运彩软件下载 automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains.

    By the end of this book, you鸿运彩软件下载’ll have discovered tips and practical solutions to all of you鸿运彩软件下载r feature engineering problems.

    Features
    • Discover solutions for feature generation, feature extraction, and feature selection
    • Uncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasets
    • Implement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy libraries
    Page Count 372
    Course Length 11 hours 9 minutes
    ISBN 9781789806311
    Date Of Publication 22 Jan 2020

    Authors

    Soledad Galli

    Soledad Galli is a lead data scientist with more than 10 years of experience in world-class academic institutions and renowned businesses. She has researched, developed, and put into production machine learning models for insurance claims, credit risk assessment, and fraud prevention. Soledad received a Data Science Leaders' award in 2018 and was named one of LinkedIn's voices in data science and analytics in 2019. She is passionate about enabling people to step into and excel in data science, which is why she mentors data scientists and speaks at data science meetings regularly. She also teaches online courses on machine learning in a prestigious Massive Open Online Course platform, which have reached more than 10,000 students worldwide.

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