Applied Predictive Modeling
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Applied Predictive Modeling
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Author : Max Kuhn
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-05-17
Applied Predictive Modeling written by Max Kuhn and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-17 with Medical categories.
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.
Applied Predictive Modeling
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Author : Max Kuhn
language : en
Publisher: Springer
Release Date : 2018-03-30
Applied Predictive Modeling written by Max Kuhn and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-30 with Medical categories.
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.
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!
Applied Predictive Modeling
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Author : Max Kuhn
language : en
Publisher:
Release Date : 2016
Applied Predictive Modeling written by Max Kuhn and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
This is a book on data analysis with a specific focus on the practice of predictive modeling, which is associated with machine learning, pattern recognition and data mining.The practive of predictive modeling defines the process of developing a model in a way that we can understand and quatify the model's prediction accuracy on future.
Applied Predictive Modeling
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Author : Max Kuhn
language : en
Publisher:
Release Date : 2024
Applied Predictive Modeling written by Max Kuhn and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with Mathematical models categories.
Applied Predictive Analytics
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Author : Dean Abbott
language : en
Publisher: John Wiley & Sons
Release Date : 2014-03-31
Applied Predictive Analytics written by Dean Abbott and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-03-31 with Computers categories.
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.
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!
Applied Predictive Modelling For Direct Marketing In Sas
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Author : Alun Brain
language : en
Publisher:
Release Date : 2018-10-24
Applied Predictive Modelling For Direct Marketing In Sas written by Alun Brain and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-24 with categories.
The authors noted that while there is so much talk about the adoption of predictive modelling within marketing and more specifically direct marketing, there is no one book in the market which takes the audience through an applied step by step approach on how to develop a predictive model. This book serves as a guide and is a hands-on learning tool for any self-starter, modeller or data scientist who wants to learn how to develop predictive models. It's a practical guide for both analysts and marketing people and walks them through the different steps of developing a predictive model. The authors believe that for anyone looking to learn predictive models, this is the one book that they will have to read. The book also touches on the softer side of predictive model development which is, how to present the models, whom to present the models, the best ways to get the model deployed for improving business outcomes.
Applied Predictive Modelling For Direct Marketing In R
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Author : Alun Brain
language : en
Publisher:
Release Date : 2018-11-24
Applied Predictive Modelling For Direct Marketing In R written by Alun Brain and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-24 with categories.
The authors noted that while there is so much talk about the adoption of predictive modelling within marketing and more specifically direct marketing, there is no one book in the market which takes the audience through an applied step by step approach on how to develop a predictive model. This book serves as a guide and is a hands-on learning tool for any self-starter or modeller who wants to learn how to develop predictive models. It's a practical guide for both analysts and marketing people and walks them through the different steps of developing a predictive model. The authors believe that for anyone looking to learn predictive models, this is the one book that they will have to read. The book also touches on the softer side of predictive model development which is, how to present the models, whom to present the models, the best ways to get the model deployed for improving business outcomes.
Applied Predictive Modelling For Marketing In Python
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Author : Sameer Rahman
language : en
Publisher:
Release Date : 2020-06-04
Applied Predictive Modelling For Marketing In Python written by Sameer Rahman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-04 with categories.
The authors noted that while there is so much talk about the adoption of predictive modelling within marketing and more specifically direct marketing, there is no one book in the market which takes the audience through an applied step by step approach on how to develop a predictive model. This book serves as a guide and is a hands-on learning tool for any self-starter, modeller or data scientist who wants to learn how to develop predictive models. It's a practical guide for both analysts and marketing people and walks them through the different steps of developing a predictive model. The authors believe that for anyone looking to learn predictive models, this is the one book that they will have to read. The book also touches on the softer side of predictive model development which is, how to present the models, whom to present the models, the best ways to get the model deployed for improving business outcomes.