Modeling Techniques In Predictive Analytics

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Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science

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Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python

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Applied Predictive Analytics - Principles and Techniques for the Professional Data Analyst John Wiley & Sons Inc

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Learn the art and science of predictive analytics -- techniques that get results§Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included.§The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today§This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions§Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish§Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios§A companion website provides all the data sets used to generate the examples as well as a free trial version of software§Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.

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Data Science and Predictive Analytics Springer, Berlin

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This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown notebooks. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings.Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analytics address specific knowledge gaps, resolve educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications. The book's fourteen chapters start with an introduction and progressively build foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition of the book includes additional learning-based strategies utilizing generative adversarial networks, transfer learning, and synthetic data generation, as well as eight complementary electronic appendices. This textbook is suitable for formal didactic instructor-guided course education, as well as for individual or team-supported self-learning. The material is presented at the upper-division and graduate-level college courses and covers applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide range of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory, funding, and policy agencies. The supporting book website provides many examples, datasets, functional scripts, complete electronic notebooks, extensive appendices, and additional materials.

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Predictive Business Analytics - Forward-Looking Capabilities to Improve Business Performance John Wiley & Sons Inc

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Discover the breakthrough tool your company can use to make winning decisions This forward-thinking book addresses the emergence of predictive business analytics, how it can help redefine the way your organization operates, and many of the misconceptions that impede the adoption of this new management capability. Filled with case examples, Predictive Business Analytics defines ways in which specific industries have applied these techniques and tools and how predictive business analytics can complement other financial applications such as budgeting, forecasting, and performance reporting.Examines how predictive business analytics can help your organization understand its various drivers of performance, their relationship to future outcomes, and improve managerial decision-making Looks at how to develop new insights and understand business performance based on extensive use of data, statistical and quantitative analysis, and explanatory and predictive modeling Written for senior financial professionals, as well as general and divisional senior management Visionary and effective, Predictive Business Analytics reveals how you can use your business's skills, technologies, tools, and processes for continuous analysis of past business performance to gain forward-looking insight and drive business decisions and actions.

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Predictive Analytics John Wiley & Sons Inc

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"Mesmerizing & fascinating..." --The Seattle Post-Intelligencer"The Freakonomics of big data." --Stein Kretsinger, founding executive of Advertising.comAward-winning | Used by over 30 universities | Translated into 9 languagesAn introduction for everyone. In this rich, fascinating -- surprisingly accessible -- introduction, leading expert Eric Siegel reveals how predictive analytics works, and how it affects everyone every day. Rather than a "how to" for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques.Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die.Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections.How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.Predictive Analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.In this lucid, captivating introduction -- now in its Revised and Updated edition -- former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction:* What type of mortgage risk Chase Bank predicted before the recession.* Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves.* Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights.* Five reasons why organizations predict death -- including one health insurance company.* How U.S. Bank and Obama for America calculated -- and Hillary for America 2016 plans to calculate -- the way to most strongly persuade each individual.* Why the NSA wants all your data: machine learning supercomputers to fight terrorism.* How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy!* How companies ascertain untold, private truths -- how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job.* How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison.* 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more.How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more.A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it -- or consumed by it -- get a handle on the power of Predictive Analytics.

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Statistical Modeling in Machine Learning Elsevier Science & Technology

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Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and rolesof statistics, exploratory data analysis and Machine Learning, and their application in various domains. The various aspects of Machine Learning are discussed along with basics of statistics in the first half of the book. Concepts are presentedwith simple examplesandgraphical representation for better understanding of techniques. Scientists, researchers, academicians, and research scholars working in the field of predictive analytics using Machine Learning and statistics can benefit from case studies of real-world applications from different domains, depicted in the latter half of the book.The book starts with fundamentals, mathematical pre-requisites and traditional approaches in order to provide readers with a solid foundation of understanding. Then advanced techniques and applications are presented along with challenges and future requirements in the field. Statistical Modeling in Machine Learning: Concepts and Applications takes a much-needed holistic approach

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Regression Modeling Strategies Springer International Publishing AG

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This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modeling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for fitting nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. The reader will gain a keen understanding of predictive accuracy and the harm of categorizing continuous predictors or outcomes. This text realistically deals with model uncertainty and its effects on inference, to achieve "safe data mining." It also presents many graphical methods for communicating complex regression models to non-statisticians.§§Regression Modeling Strategies presents full-scale case studies of non-trivial datasets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalized least squares for serial (repeated measurement) data, the binary logistic model, models for ordinal responses, parametric survival regression models and the Cox semi parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression.§§As in the first edition, this text is intended for Masters' or Ph.D. level graduate students who have had a general introductory probability and statistics course and who are well versed in ordinary multiple regression and intermediate algebra. The book will also serve as a reference for data analysts and statistical methodologists, as it contains an up-to-date survey and bibliography of modern statistical modeling techniques. Examples used in the text mostly come from biomedical research, but the methods are applicable anywhere predictive models ("analytics") are useful, including economics, epidemiology, sociology, psychology, engineering and marketing.§

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Financial Modeling with Crystal Ball and Excel, + Website 2e John Wiley & Sons Inc

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Updated look at financial modeling and Monte Carlo simulation with software by Oracle Crystal Ball This revised and updated edition of the bestselling book on financial modeling provides the tools and techniques needed to perform spreadsheet simulation. It answers the essential question of why risk analysis is vital to the decision-making process, for any problem posed in finance and investment. This reliable resource reviews the basics and covers how to define and refine probability distributions in financial modeling, and explores the concepts driving the simulation modeling process. It also discusses simulation controls and analysis of simulation results. The second edition of Financial Modeling with Crystal Ball and Excel contains instructions, theory, and practical example models to help apply risk analysis to such areas as derivative pricing, cost estimation, portfolio allocation and optimization, credit risk, and cash flow analysis. It includes the resources needed to develop essential skills in the areas of valuation, pricing, hedging, trading, risk management, project evaluation, credit risk, and portfolio management.* Offers an updated edition of the bestselling book covering the newest version of Oracle Crystal Ball* Contains valuable insights on Monte Carlo simulation-an essential skill applied by many corporate finance and investment professionals* Written by John Charnes, the former finance department chair at the University of Kansas and senior vice president of global portfolio strategies at Bank of America, who is currently President and Chief Data Scientist at Syntelli Solutions, Inc. Risk Analytics and Predictive Intelligence Division (Syntelli RAPID) Engaging and informative, this book is a vital resource designed to help you become more adept at financial modeling and simulation.

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Marketing Data Science Pearson Education

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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.

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