Applied Predictive Modeling

Author: Max Kuhn
Publisher: Springer
ISBN: 1461468485
Size: 49.21 MB
Format: PDF, Mobi
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Winner of the 2014 Technometrics Ziegel Prize for Outstanding Book

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.  Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance―all of which are problems that occur frequently in practice.
 
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.  The data sets and corresponding code are available in the book's companion AppliedPredictiveModeling R package, which is freely available on the CRAN archive.
 
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.
 
Readers and students interested in implementing the methods should have some basic knowledge of R.  And a handful of the more advanced topics require some mathematical knowledge.

92 Applied Predictive Modeling Techniques In R With Step By Step Instructions On How To Build Them Fast

Author: Dr. N D Lewis
Publisher: CreateSpace Independent Publishing Platform
ISBN: 151751679X
Size: 60.55 MB
Format: PDF, ePub, Mobi
View: 5447
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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!


R For Data Science Import Tidy Transform Visualize And Model Data

Author: Hadley Wickham
Publisher: O'Reilly Media
ISBN: 1491910399
Size: 30.19 MB
Format: PDF
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Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.

Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way.

You’ll learn how to:

  • Wrangle—transform your datasets into a form convenient for analysis
  • Program—learn powerful R tools for solving data problems with greater clarity and ease
  • Explore—examine your data, generate hypotheses, and quickly test them
  • Model—provide a low-dimensional summary that captures true "signals" in your dataset
  • Communicate—learn R Markdown for integrating prose, code, and results

An Introduction To Statistical Learning With Applications In R Springer Texts In Statistics

Author: Gareth James
Publisher: Springer
ISBN: 1461471370
Size: 10.80 MB
Format: PDF
View: 5276
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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.


Modeling Techniques In Predictive Analytics Business Problems And Solutions With R Revised And Expanded Edition Ft Press Analytics

Author: Thomas W. Miller
Publisher: Pearson FT Press
ISBN: 0133886018
Size: 49.90 MB
Format: PDF, ePub, Mobi
View: 2908
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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

 

 


The Elements Of Statistical Learning Data Mining Inference And Prediction Second Edition Springer Series In Statistics

Author: Trevor Hastie
Publisher: Springer
ISBN: 0387848576
Size: 10.10 MB
Format: PDF, ePub
View: 5450
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This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.


Fundamentals Of Machine Learning For Predictive Data Analytics Algorithms Worked Examples And Case Studies Mit Press

Author: John D. Kelleher
Publisher: The MIT Press
ISBN: 0262029448
Size: 16.26 MB
Format: PDF, Docs
View: 1637
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Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.

After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.


Deep Learning Adaptive Computation And Machine Learning Series

Author: Ian Goodfellow
Publisher: The MIT Press
ISBN: 0262035618
Size: 72.37 MB
Format: PDF, Docs
View: 5981
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"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.

Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.


A Solution Manual And Notes For Applied Predictive Modeling By Max Kuhn And Kjell Johnson

Author: John Weatherwax
Publisher:
ISBN: B00Z1DSDUU
Size: 23.66 MB
Format: PDF, Kindle
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As modern data scientists in competitive fields it becomes more and more important to stay current with the most recent techniques in statistical modeling and to learn best practices from the masters in the field. The book ``Applied
Predictive Modeling'' by Kuhn and Johnson provides the reader with exactly that type of information. Full of practical, helpful, and pertinent advice its exactly the type of book anyone with an interest in data mining, predictive modeling, or machine learning should be reading. Because of its strong standing in the community I wanted to learn this material as well as possible. To do this, I read and then worked all of the problems in each chapter. That work produced the
solution manual you find here. Whether you are looking at this book for a class or for your own self-study, having access to this material should help verify and solidify the concepts presented; in the end making you a much stronger statistical modeler.

This pages is for the kindle version of the solution manual. There is also a PDF version available from my website.

Hands On Machine Learning With Scikit Learn And Tensorflow Concepts Tools And Techniques To Build Intelligent Systems

Author: Aurélien Géron
Publisher: O'Reilly Media
ISBN: 1491962291
Size: 62.67 MB
Format: PDF, ePub
View: 5081
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Graphics in this book are printed in black and white.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

  • Explore the machine learning landscape, particularly neural nets
  • Use scikit-learn to track an example machine-learning project end-to-end
  • Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
  • Use the TensorFlow library to build and train neural nets
  • Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
  • Learn techniques for training and scaling deep neural nets
  • Apply practical code examples without acquiring excessive machine learning theory or algorithm details

Applied Predictive Modeling

Author: Max Kuhn
Publisher: Springer Science & Business Media
ISBN: 1461468493
Size: 74.75 MB
Format: PDF, ePub
View: 7124
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While the text is biased against complex equations, a mathematical background is needed for advanced topics. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them.

Applied Predictive Modeling

Author: Max Kuhn
Publisher: Springer
ISBN: 9781461468486
Size: 26.47 MB
Format: PDF, Mobi
View: 3452
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While the text is biased against complex equations, a mathematical background is needed for advanced topics. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them.

Applied Predictive Analytics

Author: Dean Abbott
Publisher: John Wiley & Sons
ISBN: 111872769X
Size: 31.38 MB
Format: PDF
View: 4941
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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 ...

92 Applied Predictive Modeling Techniques In R

Author: N. D. Lewis
Publisher: CreateSpace
ISBN: 9781517516796
Size: 28.98 MB
Format: PDF, ePub, Docs
View: 6470
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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.

Applied Predictive Control

Author: Sunan Huang
Publisher: Springer Science & Business Media
ISBN: 9781852333386
Size: 51.75 MB
Format: PDF, Mobi
View: 5388
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Three main schemes for advanced predictive control are addressed in this book: • Smith Predictive Control; • Generalised Predictive Control; • a form of predictive control based on Finite Spectrum Assignment.

Modeling Techniques In Predictive Analytics

Author: Thomas W. Miller
Publisher: FT Press
ISBN: 0133412954
Size: 36.40 MB
Format: PDF, ePub
View: 2313
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Today, successful firms compete and win based on analytics. Modeling Techniques in Predictive Analytics brings together all the concepts, techniques, and R code you need to excel in any role involving analytics.

Regression Modeling Strategies

Author: Frank Harrell
Publisher: Springer
ISBN: 3319194259
Size: 53.11 MB
Format: PDF, ePub
View: 289
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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.

Predictive Modeling Applications In Actuarial Science

Author: Edward W. Frees
Publisher: Cambridge University Press
ISBN: 1107029872
Size: 78.39 MB
Format: PDF, ePub, Mobi
View: 6718
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This book is for actuaries and financial analysts developing their expertise in statistics and who wish to become familiar with concrete examples of predictive modeling.