Modeling Techniques In Predictive Analytics With Python And R A Guide To Data Science

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

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Author by : Thomas W. Miller
Languange Used : English, France, Spanish, Italia and chinese
Page : 448
Isbn : 013389214X
Identifier : 9780133892147
Release : 2014-09-29
Publisher by : FT Press
Category : Computers
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Description : Read Now Modeling Techniques In Predictive Analytics With Python And R by Thomas W. Miller and you can download with pub, pdf, txt, doc, and more file format with free account. 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 Modeling Techniques in Predictive Analytics with R and Python

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Author by : Thomas W. Miller
Languange Used : English, France, Spanish, Italia and chinese
Page : 225
Isbn : 0133887340
Identifier : 9780133887341
Release : 2015-05-02
Publisher by : FT Press
Category : Business & Economics
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Description : Read Now Marketing Data Science by Thomas W. Miller and you can download with pub, pdf, txt, doc, and more file format with free account. 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.

Sports Analytics and Data Science Winning the Game with Methods and Models

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Author by : Thomas W. Miller
Languange Used : English, France, Spanish, Italia and chinese
Page : 225
Isbn : 0133887413
Identifier : 9780133887419
Release : 2015-11-18
Publisher by : FT Press
Category : Computers
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Description : Read Now Sports Analytics And Data Science by Thomas W. Miller and you can download with pub, pdf, txt, doc, and more file format with free account. this is the ebook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. this up-to-the-minute reference will help you master all three facets of sports analytics — and use it to win! sports analytics and data science is the most accessible and practical guide to sports analytics for everyone who cares about winning and everyone who is interested in data science. you’ll discover how successful sports analytics blends business and sports savvy, modern information technology, and sophisticated modeling techniques. you’ll master the discipline through realistic sports vignettes and intuitive data visualizations–not complex math. every chapter focuses on one key sports analytics application. miller guides you through assessing players and teams, predicting scores and making game-day decisions, crafting brands and marketing messages, increasing revenue and profitability, and much more. step by step, you’ll learn how analysts transform raw data and analytical models into wins: both on the field and in any sports business.

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

Download Marketing Data Science Book or Ebook File with PDF Epub Audio and Full format File

Author by : Thomas W. Miller
Languange Used : English, France, Spanish, Italia and chinese
Page : 225
Isbn : 0133887340
Identifier : 9780133887341
Release : 2015-05-02
Publisher by : FT Press
Category : Business & Economics
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Description : Read Now Marketing Data Science by Thomas W. Miller and you can download with pub, pdf, txt, doc, and more file format with free account. 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 Business Problems and Solutions with R, Revised and Expanded Edition

Download Modeling Techniques In Predictive Analytics Book or Ebook File with PDF Epub Audio and Full format File

Author by : Thomas W. Miller
Languange Used : English, France, Spanish, Italia and chinese
Page : 384
Isbn : 0133886190
Identifier : 9780133886191
Release : 2014-09-29
Publisher by : FT Press
Category : Computers
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Description : Read Now Modeling Techniques In Predictive Analytics by Thomas W. Miller and you can download with pub, pdf, txt, doc, and more file format with free account. 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

Web and Network Data Science Modeling Techniques in Predictive Analytics

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Author by : Thomas W. Miller
Languange Used : English, France, Spanish, Italia and chinese
Page : 384
Isbn : 0133887642
Identifier : 9780133887648
Release : 2014-12-19
Publisher by : FT Press
Category : Computers
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Description : Read Now Web And Network Data Science by Thomas W. Miller and you can download with pub, pdf, txt, doc, and more file format with free account. 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.

Learning Predictive Analytics with Python

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Author by : Ashish Kumar
Languange Used : English, France, Spanish, Italia and chinese
Page : 354
Isbn : 1783983272
Identifier : 9781783983278
Release : 2016-02-15
Publisher by : Packt Publishing Ltd
Category : Computers
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Description : Read Now Learning Predictive Analytics With Python by Ashish Kumar and you can download with pub, pdf, txt, doc, and more file format with free account. gain practical insights into predictive modelling by implementing predictive analytics algorithms on public datasets with python about this book a step-by-step guide to predictive modeling including lots of tips, tricks, and best practices get to grips with the basics of predictive analytics with python learn how to use the popular predictive modeling algorithms such as linear regression, decision trees, logistic regression, and clustering who this book is for if you wish to learn how to implement predictive analytics algorithms using python libraries, then this is the book for you. if you are familiar with coding in python (or some other programming/statistical/scripting language) but have never used or read about predictive analytics algorithms, this book will also help you. the book will be beneficial to and can be read by any data science enthusiasts. some familiarity with python will be useful to get the most out of this book, but it is certainly not a prerequisite. what you will learn understand the statistical and mathematical concepts behind predictive analytics algorithms and implement predictive analytics algorithms using python libraries analyze the result parameters arising from the implementation of predictive analytics algorithms write python modules/functions from scratch to execute segments or the whole of these algorithms recognize and mitigate various contingencies and issues related to the implementation of predictive analytics algorithms get to know various methods of importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and numpy create dummy datasets and simple mathematical simulations using the python numpy and pandas libraries understand the best practices while handling datasets in python and creating predictive models out of them in detail social media and the internet of things have resulted in an avalanche of data. data is powerful but not in its raw form - it needs to be processed and modeled, and python is one of the most robust tools out there to do so. it has an array of packages for predictive modeling and a suite of ides to choose from. learning to predict who would win, lose, buy, lie, or die with python is an indispensable skill set to have in this data age. this book is your guide to getting started with predictive analytics using python. you will see how to process data and make predictive models from it. we balance both statistical and mathematical concepts, and implement them in python using libraries such as pandas, scikit-learn, and numpy. you'll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. you will also learn more about the best predictive modeling algorithms such as linear regression, decision trees, and logistic regression. finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. style and approach all the concepts in this book been explained and illustrated using a dataset, and in a step-by-step manner. the python code snippet to implement a method or concept is followed by the output, such as charts, dataset heads, pictures, and so on. the statistical concepts are explained in detail wherever required.

Python for R Users A Data Science Approach

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Author by : Ajay Ohri
Languange Used : English, France, Spanish, Italia and chinese
Page : 368
Isbn : 1119126762
Identifier : 9781119126768
Release : 2017-11-13
Publisher by : John Wiley & Sons
Category : Computers
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Description : Read Now Python For R Users by Ajay Ohri and you can download with pub, pdf, txt, doc, and more file format with free account. the definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both r and python the first book of its kind, python for r users: a data science approach makes it easy for r programmers to code in python and python users to program in r. short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of r to python and python to r. following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. statistical modeling, machine learning, and data mining—including supervised and unsupervised data mining methods—are treated in detail, as are time series forecasting, text mining, and natural language processing. • features a quick-learning format with concise tutorials and actionable analytics • provides command-by-command translations of r to python and vice versa • incorporates python and r code throughout to make it easier for readers to compare and contrast features in both languages • offers numerous comparative examples and applications in both programming languages • designed for use for practitioners and students that know one language and want to learn the other • supplies slides useful for teaching and learning either software on a companion website python for r users: a data science approach is a valuable working resource for computer scientists and data scientists that know r and would like to learn python or are familiar with python and want to learn r. it also functions as textbook for students of computer science and statistics. a. ohri is the founder of decisionstats.com and currently works as a senior data scientist. he has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. mr. ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. his other books include r for business analytics and r for cloud computing.

Machine Learning in Python Essential Techniques for Predictive Analysis

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Author by : Michael Bowles
Languange Used : English, France, Spanish, Italia and chinese
Page : 336
Isbn : 1118961749
Identifier : 9781118961742
Release : 2015-03-30
Publisher by : John Wiley & Sons
Category : Computers
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Description : Read Now Machine Learning In Python by Michael Bowles and you can download with pub, pdf, txt, doc, and more file format with free account. this book shows readers how they can successfully analyze data using only two core machine learning algorithms---and how to do so using the popular python programming language. these algorithms deal with common scenarios faced by all data analysts and data scientists. this book focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. this type of problem covers a multitude of use cases (what ad to place on a web page, predicting prices in securities markets, detecting credit card fraud, etc.). the focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. then the code examples serve to illustrate the workings of the machinery with specific hackable code. the author will explain in simple terms, using no complex math, how these algorithms work, and will then show how to apply them in python. he will also provide advice on how to select from among these algorithms, and will show how to prepare the data, and how to use the trained models in practice. the author begins with an overview of the two core algorithms, explaining the types of problems solved by each one. he then introduces a core set of python programming techniques that can be used to apply these algorithms. the author shows various techniques for building predictive models that solve a range of problems, from simple to complex; he also shows how to measure the performance of each model to ensure you use the right one. the following chapters provide a deep dive into each of the two algorithms: penalized linear regression and ensemble methods. chapters will show how to apply each algorithm in python. readers can directly use the sample code to build their own solutions.

Mastering Machine Learning with Python in Six Steps A Practical Implementation Guide to Predictive Data Analytics Using Python

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Author by : Manohar Swamynathan
Languange Used : English, France, Spanish, Italia and chinese
Page : 358
Isbn : 1484228669
Identifier : 9781484228661
Release : 2017-06-05
Publisher by : Apress
Category : Computers
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Description : Read Now Mastering Machine Learning With Python In Six Steps by Manohar Swamynathan and you can download with pub, pdf, txt, doc, and more file format with free account. master machine learning with python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. this book’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away. mastering machine learning with python in six steps presents each topic in two parts: theoretical concepts and practical implementation using suitable python packages. you’ll learn the fundamentals of python programming language, machine learning history, evolution, and the system development frameworks. key data mining/analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in scikit-learn are also covered. finally, you’ll explore advanced text mining techniques, neural networks and deep learning techniques, and their implementation. all the code presented in the book will be available in the form of ipython notebooks to enable you to try out these examples and extend them to your advantage. what you'll learn examine the fundamentals of python programming language review machine learning history and evolution understand machine learning system development frameworks implement supervised/unsupervised/reinforcement learning techniques with examples explore fundamental to advanced text mining techniques implement various deep learning frameworks who this book is for python developers or data engineers looking to expand their knowledge or career into machine learning area. non-python (r, sas, spss, matlab or any other language) machine learning practitioners looking to expand their implementation skills in python. novice machine learning practitioners looking to learn advanced topics, such as hyperparameter tuning, various ensemble techniques, natural language processing (nlp), deep learning, and basics of reinforcement learning.

Effective CRM Using Predictive Analytics

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Author by : Antonios Chorianopoulos
Languange Used : English, France, Spanish, Italia and chinese
Page : 392
Isbn : 1119011558
Identifier : 9781119011552
Release : 2016-01-19
Publisher by : John Wiley & Sons
Category : BUSINESS & ECONOMICS
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Description : Read Now Effective Crm Using Predictive Analytics by Antonios Chorianopoulos and you can download with pub, pdf, txt, doc, and more file format with free account. a step-by-step guide to data mining applications in crm. following a handbook approach, this book bridges the gap between analytics and their use in everyday marketing, providing guidance on solving real business problems using data mining techniques. the book is organized into three parts. part one provides a methodological roadmap, covering both the business and the technical aspects. the data mining process is presented in detail along with specific guidelines for the development of optimized acquisition, cross/ deep/ up selling and retention campaigns, as well as effective customer segmentation schemes. additionally, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise. in part two, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise. part three is packed with real world case studies which employ the use of three leading data mining tools: ibm spss modeler, rapidminer and data mining for excel. case studies from industries including banking, retail and telecommunications are presented in detail so as to serve as templates for developing similar applications. key features: includes numerous real-world case studies which are presented step by step, demystifying the usage of data mining models and clarifying all the methodological issues. topics are presented with the use of three leading data mining tools: ibm spss modeler, rapidminer and data mining for excel. accompanied by a website featuring material from each case study, including datasets and relevant code. combining data mining and business knowledge, this practical book provides all the necessary information for designing, setting up, executing and deploying data mining techniques in crm. effective crm using predictive analytics will benefit data mining practitioners and consultants, data analysts, statisticians, and crm officers. the book will also be useful to academics and students interested in applied data mining.

Applied Predictive Modeling

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Author by : Max Kuhn
Languange Used : English, France, Spanish, Italia and chinese
Page : 600
Isbn : 1461468493
Identifier : 9781461468493
Release : 2013-05-17
Publisher by : Springer Science & Business Media
Category : Medical
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Description : Read Now Applied Predictive Modeling by Max Kuhn and you can download with pub, pdf, txt, doc, and more file format with free account. applied predictive modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. the text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. the text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive r code for each step of the process. this multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. to that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s r package. this text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. while the text is biased against complex equations, a mathematical background is needed for advanced topics.