Modeling Techniques In Predictive Analytics
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Modeling Techniques In Predictive Analytics
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Author : Thomas W. Miller
language : en
Publisher: FT Press
Release Date : 2014-09-29
Modeling Techniques In Predictive Analytics written by Thomas W. Miller and has been published by FT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-29 with Business & Economics categories.
To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods. Gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more
Modeling Techniques In Predictive Analytics With Python And R
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Author : Thomas W. Miller
language : en
Publisher: FT Press
Release Date : 2014-09-29
Modeling Techniques In Predictive Analytics With Python And R written by Thomas W. Miller and has been published by FT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-29 with Business & Economics categories.
Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more
Marketing Data Science
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Author : Thomas W. Miller
language : en
Publisher: FT Press
Release Date : 2015-05-02
Marketing Data Science written by Thomas W. Miller and has been published by FT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-02 with Business & Economics categories.
Now, a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications. Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis. Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web – and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R. Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance.
Modeling Techniques In Predictive Analytics
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Author : Thomas W. Miller
language : en
Publisher:
Release Date : 2014
Modeling Techniques In Predictive Analytics written by Thomas W. Miller and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Business planning categories.
Modeling Techniques In Predictive Analystics With Python And R
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Author : Thomas W. Miller
language : en
Publisher:
Release Date : 2014
Modeling Techniques In Predictive Analystics With Python And R written by Thomas W. Miller and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Business planning categories.
Marketing Data Science
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Author : Thomas W. Miller
language : en
Publisher:
Release Date : 2015
Marketing Data Science written by Thomas W. Miller and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Data mining categories.
Web And Network Data Science
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Author : Thomas W. Miller
language : en
Publisher: FT Press
Release Date : 2014-12-19
Web And Network Data Science written by Thomas W. Miller and has been published by FT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-19 with Business & Economics categories.
Master modern web and network data modeling: both theory and applications. In Web and Network Data Science, a top faculty member of Northwestern University’s prestigious analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling for predictive analytics. Some books in this field focus either entirely on business issues (e.g., Google Analytics and SEO); others are strictly academic (covering topics such as sociology, complexity theory, ecology, applied physics, and economics). This text gives today's managers and students what they really need: integrated coverage of concepts, principles, and theory in the context of real-world applications. Building on his pioneering Web Analytics course at Northwestern University, Thomas W. Miller covers usability testing, Web site performance, usage analysis, social media platforms, search engine optimization (SEO), and many other topics. He balances this practical coverage with accessible and up-to-date introductions to both social network analysis and network science, demonstrating how these disciplines can be used to solve real business problems.
92 Applied Predictive Modeling Techniques In R
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Author : N. D. Lewis
language : en
Publisher: CreateSpace
Release Date : 2015-10-21
92 Applied Predictive Modeling Techniques In R written by N. D. Lewis and has been published by CreateSpace this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-21 with categories.
About This Book This jam-packed book takes you under the hood with step by step instructions using the popular and free R predictive analytics package. It provides numerous examples, illustrations and exclusive use of real data to help you leverage the power of predictive analytics. A book for every data analyst, student and applied researcher. Here is what it can do for you: BOOST PRODUCTIVITY: Learn how to build predictive analytic models in less time than you ever imagined possible! Even if you're a busy professional or a student with little time. By spending as little as 10 minutes a day working through the dozens of real world examples, illustrations, practitioner tips and notes, you'll be able to make giant leaps forward in your knowledge, strengthen your business performance, broaden your skill-set and improve your understanding. SIMPLIFY ANALYSIS: You will discover over 90 easy to follow applied predictive analytic techniques that can instantly expand your modeling capability. Plus you'll discover simple routines that serve as a check list you repeat next time you need a specific model. Even better, you'll discover practitioner tips, work with real data and receive suggestions that will speed up your progress. So even if you're completely stressed out by data, you'll still find in this book tips, suggestions and helpful advice that will ease your journey through the data science maze. SAVE TIME: Imagine having at your fingertips easy access to the very best of predictive analytics. In this book, you'll learn fast effective ways to build powerful models using R. LEARN FASTER: 92 Applied Predictive Modeling Techniques in R offers a practical results orientated approach that will boost your productivity, expand your knowledge and create new and exciting opportunities for you to get the very best from your data. IMPROVE RESULTS: Want to improve your predictive analytic results, but don't have enough time? Right now there are a dozen ways to instantly improve your predictive models performance. Odds are, these techniques will only take a few minutes apiece to complete. The problem? You might feel like there's not enough time to learn how to do them all. The solution is in your hands. It uses R, which is free, open-source, and extremely powerful software. Here is some of what is included: Support Vector Machines Relevance Vector Machines Neural networks Random forests Random ferns Classical Boosting Model based boosting Decision trees Cluster Analysis For people interested in statistics, machine learning, data analysis, data mining, and future hands-on practitioners seeking a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. Buy the book today. Your next big breakthrough using predictive analytics is only a page away!
Managing Digital And Media Organizations
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Author : Ramesh Kotwal
language : en
Publisher: Educohack Press
Release Date : 2025-02-20
Managing Digital And Media Organizations written by Ramesh Kotwal and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Performing Arts categories.
"Managing Digital and Media Organizations" is a comprehensive guide for navigating the dynamic world of media and digital businesses. We focus on the U.S. within a global context, beginning with the history of these industries, from traditional formats to digital dominance. We identify key trends and challenges companies must adapt to in this evolving landscape. Understanding the functioning of media and digital companies is crucial. We explore different organizational structures, from hierarchies to agile models, emphasizing the importance of a collaborative and adaptable digital culture for success. Leading and managing in this digital age presents unique challenges. We equip readers with real-world examples and practical strategies to address these challenges, drive innovation, and lead effectively. Content creation and audience engagement are vital. We delve into strategies for developing resonant content and building connections with audiences in the digital realm. Legal and ethical considerations, such as intellectual property rights and responsible content practices, are also tackled. We cover financial management and monetization tactics specific to media and digital businesses, including budgeting and revenue models. Looking ahead, we explore future trends that will shape these organizations. In essence, "Managing Digital and Media Organizations" provides practical knowledge and a global perspective to thrive in an increasingly digital world.
Applied Predictive Modeling
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Author : Steven Taylor
language : en
Publisher: Steven Taylor
Release Date : 2020-07-14
Applied Predictive Modeling written by Steven Taylor and has been published by Steven Taylor this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-14 with Science categories.
Applied Predictive Modeling Predictive modeling uses statistics in order to predict outcomes. However, predictive modeling can be applied to future and to any other kind of unknown event, regardless of when it happened. When it comes to the applications of predictive modeling, techniques are used in various fields including algorithmic trading, uplift modeling, archaeology, health care, customer relationship management and many others. This book covers the predictive modeling process with fundamental steps of the process, data preprocessing, data splitting and crucial steps of model tuning and improving model performance. Further, the book will introduce you to the most common classification and regression techniques including logistic regression which is widely used when it comes to the finding the probability of event success or event failure. You will get to know the common predictive modeling techniques as well such as stepwise regression, polynomial regression and ridge regression which will help you when you are dealing with the data that suffers from very common multicollinearity where independent variables are highly correlated. The text then provides fundamental steps to effective predictive modeling. In the second chapter, you will learn how to build your own predictive model with logistic regression and Python. You will find data sets as well as corresponding codes. On of the crucial predictive modeling steps is model tuning, so you will learn some common techniques used in order to improve your model performance. You will get to know how to tune the parameters commonly used to increase the overall predictive power. Predictive modeling comes with a few obstacles and challenges like class imbalance. Imbalanced classes commonly put the accuracy of the model out of business, but you will learn how to properly handle class imbalance which will significantly improve the accuracy of your model. The book is multi-purpose focused on to predictive modeling process and predictive modeling techniques, so it will be of great help for those who are interested in predictive modeling techniques and applications. So, it is the right time to simplify the analysis, boost productivity as well as save time. The book will be your companion on your journey towards highly accurate predictive models. What you will learn in Applied Predictive Modeling: Most common predictive modeling techniques Types of regression models The overall predictive modeling process Fundamental steps to effective and highly accurate predictive modeling How to build predictive model with logistic regression with code listings How to build predictive model using Python How to enhance your model performance Parameters for increasing the overall predictive power How to handle class imbalance Common causes of poor model performance Get this book now and learn more about Applied Predictive Modeling!