Feature Engineering For Machine Learning And Data Analytics
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Feature Engineering For Machine Learning And Data Analytics
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Author : Guozhu Dong
language : en
Publisher: CRC Press
Release Date : 2018-03-14
Feature Engineering For Machine Learning And Data Analytics written by Guozhu Dong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-14 with Business & Economics categories.
Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.
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Feature Engineering Optimizing Data For Ml Success
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Author : Isandro Myles
language : en
Publisher: Independently Published
Release Date : 2025-09-09
Feature Engineering Optimizing Data For Ml Success written by Isandro Myles and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-09 with Computers categories.
Unlock the true potential of your data for machine learning success. In Feature Engineering, you'll learn the essential techniques to optimize your data and enhance machine learning model performance. This step-by-step guide dives deep into the process of transforming raw data into powerful features that drive more accurate predictions and better outcomes. Whether you're a beginner or experienced data scientist, this book will help you refine your skills in data preparation and feature selection for machine learning. Inside, you'll learn how to: Understand the importance of feature engineering: how it impacts model accuracy, generalization, and overall performance. Prepare and clean data: handle missing values, outliers, duplicates, and imbalanced data to ensure quality input for your models. Transform raw data into useful features through scaling, encoding, and binning techniques. Create new features using domain knowledge, interaction terms, and aggregation for richer data representations. Implement feature selection techniques: reduce dimensionality with methods like mutual information, correlation analysis, and L1 regularization. Extract and engineer time-series features for applications in stock prediction, forecasting, and IoT. Use advanced feature engineering techniques like principal component analysis (PCA), feature importance, and automated feature engineering tools. Work with text and categorical data: apply NLP methods for feature extraction and transform textual data into valuable input features. Leverage feature scaling and normalization techniques like min-max scaling and z-score standardization. Evaluate the impact of feature engineering on model performance using cross-validation and A/B testing. With hands-on tutorials, real-world examples, and best practices for handling complex datasets, this book helps you turn raw data into meaningful features that significantly improve your machine learning models. Who This Book Is For Data scientists and ML engineers looking to improve model performance through data preparation Beginner and intermediate machine learning practitioners interested in mastering feature engineering techniques Business analysts and entrepreneurs who want to better leverage data for decision-making Students and researchers focused on applied machine learning and data analytics Developers looking to implement more efficient and accurate machine learning solutions Master the art of feature engineering and elevate your machine learning models to new heights.
Building Feature Extraction With Machine Learning
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Author : Bharath.H. Aithal
language : en
Publisher: CRC Press
Release Date : 2022-12-29
Building Feature Extraction With Machine Learning written by Bharath.H. Aithal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-29 with Technology & Engineering categories.
Big geospatial datasets created by large infrastructure projects require massive computing resources to process. Feature extraction is a process used to reduce the initial set of raw data for manageable image processing, and machine learning (ML) is the science that supports it. This book focuses on feature extraction methods for optical geospatial data using ML. It is a practical guide for professionals and graduate students who are starting a career in information extraction. It explains spatial feature extraction in an easy-to-understand way and includes real case studies on how to collect height values for spatial features, how to develop 3D models in a map context, and others. Features Provides the basics of feature extraction methods and applications along with the fundamentals of machine learning Discusses in detail the application of machine learning techniques in geospatial building feature extraction Explains the methods for estimating object height from optical satellite remote sensing images using Python Includes case studies that demonstrate the use of machine learning models for building footprint extraction and photogrammetric methods for height assessment Highlights the potential of machine learning and geospatial technology for future project developments This book will be of interest to professionals, researchers, and graduate students in geoscience and earth observation, machine learning and data science, civil engineers, and urban planners.
Feature Engineering For Modern Machine Learning With Scikit Learn
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Author : Cuantum Technologies
language : en
Publisher: Staten House
Release Date : 2024-11-06
Feature Engineering For Modern Machine Learning With Scikit Learn written by Cuantum Technologies and has been published by Staten House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-06 with Computers categories.
This Book grants Free Access to our e-learning Platform, which includes: ✅ Free Repository Code with all code blocks used in this book ✅ Access to Free Chapters of all our library of programming published books ✅ Free premium customer support ✅ Much more... Unleash the Power of Feature Engineering for Cutting-Edge Machine Learning Transform raw data into powerful features with Feature Engineering for Modern Machine Learning with Scikit-Learn: Advanced Data Science and Practical Applications. This essential guide takes you beyond the basics, teaching you how to create, optimize, and automate features that elevate machine learning models. With a focus on real-world applications and advanced techniques, this book equips data scientists, machine learning engineers, and analytics professionals with the skills to make impactful, data-driven decisions. Why Advanced Feature Engineering is Essential In machine learning, the quality of input data determines the quality of output predictions. Advanced feature engineering is the key to uncovering hidden patterns and meaningful insights in your data, transforming it into structured inputs that drive model performance. This book provides a deep dive into creating and refining features tailored to your data's unique challenges, ensuring models are both accurate and insightful. What You'll Discover Inside Feature Engineering for Modern Machine Learning with Scikit-Learn covers every stage of advanced feature engineering, from foundational transformations to automated pipelines and cutting-edge tools: Automating Data Preparation with Scikit-Learn Pipelines: Learn to create reproducible, automated workflows that handle everything from scaling and encoding to feature selection. Advanced Feature Creation and Transformation: Master complex techniques like polynomial features, interaction terms, and dimensionality reduction, all designed to improve model accuracy. Industry-Specific Case Studies: Apply feature engineering techniques to real-world domains like healthcare, retail, and customer segmentation, gaining insights into how feature engineering adapts across fields. Modern Tools and Automation with AutoML: Explore AutoML tools like TPOT and Auto-sklearn to automate feature selection and model optimization, allowing you to focus on the highest-impact features. Deep Learning Feature Engineering: Discover techniques tailored for neural networks, including data augmentation, embeddings, and feature transformations that enhance deep learning workflows. Who Should Read This Book Whether you're an experienced data scientist or an advanced beginner looking to build cutting-edge skills, this book provides essential techniques for modern machine learning. It's ideal for anyone who wants to: Maximize model performance through impactful feature engineering. Build efficient, reproducible workflows with Scikit-Learn. Explore advanced applications across multiple domains. Elevate Your Models with Advanced Feature Engineering Feature Engineering for Modern Machine Learning with Scikit-Learn is more than just a guide-it's a toolkit for creating the data transformations that drive high-performing models. Equip yourself with the latest techniques, tools, and insights to confidently tackle real-world data science challenges and unlock the full potential of your machine learning projects. Dive into the world of feature engineering and elevate your data science expertise today!
Feature Engineering Made Easy
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Author : Sinan Ozdemir
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-01-22
Feature Engineering Made Easy written by Sinan Ozdemir and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-22 with Computers categories.
A perfect guide to speed up the predicting power of machine learning algorithms Key Features Design, discover, and create dynamic, efficient features for your machine learning application Understand your data in-depth and derive astonishing data insights with the help of this Guide Grasp powerful feature-engineering techniques and build machine learning systems Book Description Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective. You will start with understanding your data—often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data. By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization. What you will learn Identify and leverage different feature types Clean features in data to improve predictive power Understand why and how to perform feature selection, and model error analysis Leverage domain knowledge to construct new features Deliver features based on mathematical insights Use machine-learning algorithms to construct features Master feature engineering and optimization Harness feature engineering for real world applications through a structured case study Who this book is for If you are a data science professional or a machine learning engineer looking to strengthen your predictive analytics model, then this book is a perfect guide for you. Some basic understanding of the machine learning concepts and Python scripting would be enough to get started with this book.
Data Analytics And Machine Learning
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Author : Pushpa Singh
language : en
Publisher: Springer Nature
Release Date : 2024-03-19
Data Analytics And Machine Learning written by Pushpa Singh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-19 with Computers categories.
This book presents an in-depth analysis of successful data-driven initiatives, highlighting how organizations have leveraged data to drive decision-making processes, optimize operations, and achieve remarkable outcomes. Through case studies, readers gain valuable insights and learn practical strategies for implementing data analytics, big data, and machine learning solutions in their own organizations. The book discusses the transformative power of data analytics and big data in various industries and sectors and how machine learning applications have revolutionized exploration by enabling advanced data analysis techniques for mapping, geospatial analysis, and environmental monitoring, enhancing our understanding of the world and its dynamic processes. This book explores how big data explosion, the power of analytics and machine learning revolution can bring new prospects and opportunities in the dynamic and data-rich landscape. It highlights the future research directions in data analytics, big data, and machine learning that explores the emerging trends, challenges, and opportunities in these fields by covering interdisciplinary approaches such as handling and analyzing real-time and streaming data.
Big Data Analytics Systems Algorithms Applications
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Author : C.S.R. Prabhu
language : en
Publisher: Springer Nature
Release Date : 2019-10-14
Big Data Analytics Systems Algorithms Applications written by C.S.R. Prabhu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-14 with Computers categories.
This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning – including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.
Agile Data Science 2 0
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Author : Russell Jurney
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-06-07
Agile Data Science 2 0 written by Russell Jurney and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-07 with Computers categories.
Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track
Big Data Infrastructure Technologies For Data Analytics
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Author : Yuri Demchenko
language : en
Publisher: Springer Nature
Release Date : 2024-10-25
Big Data Infrastructure Technologies For Data Analytics written by Yuri Demchenko and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-25 with Computers categories.
This book provides a comprehensive overview and introduction to Big Data Infrastructure technologies, existing cloud-based platforms, and tools for Big Data processing and data analytics, combining both a conceptual approach in architecture design and a practical approach in technology selection and project implementation. Readers will learn the core functionality of major Big Data Infrastructure components and how they integrate to form a coherent solution with business benefits. Specific attention will be given to understanding and using the major Big Data platform Apache Hadoop ecosystem, its main functional components MapReduce, HBase, Hive, Pig, Spark and streaming analytics. The book includes topics related to enterprise and research data management and governance and explains modern approaches to cloud and Big Data security and compliance. The book covers two knowledge areas defined in the EDISON Data Science Framework (EDSF): Data Science Engineering and Data Management and Governance and can be used as a textbook for university courses or provide a basis for practitioners for further self-study and practical use of Big Data technologies and competent evaluation and implementation of practical projects in their organizations.