Book Description
Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts
In Detail
R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems.
This book is designed to be both a guide and a reference for moving beyond the basics of predictive modeling. The book begins with a dedicated chapter on the language of models and the predictive modeling process. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real world data sets.
By the end of this book, you will have explored and tested the most popular modeling techniques in use on real world data sets and mastered a diverse range of techniques in predictive analytics.
What You Will Learn
 Master the steps involved in the predictive modeling process
 Learn how to classify predictive models and distinguish which models are suitable for a particular problem
 Understand how and why each predictive model works
 Recognize the assumptions, strengths, and weaknesses of a predictive model, and that there is no best model for every problem
 Select appropriate metrics to assess the performance of different types of predictive model
 Diagnose performance and accuracy problems when they arise and learn how to deal with them
 Grow your expertise in using R and its diverse range of packages
Table of Contents

Mastering Predictive Analytics with R
 Table of Contents
 Mastering Predictive Analytics with R
 Credits
 About the Author
 Acknowledgments
 About the Reviewers
 www.PacktPub.com
 Preface

1. Gearing Up for Predictive Modeling
 Models
 Types of models
 The process of predictive modeling
 Performance metrics
 Summary
 2. Linear Regression
 3. Logistic Regression
 4. Neural Networks
 5. Support Vector Machines
 6. Treebased Methods
 7. Ensemble Methods
 8. Probabilistic Graphical Models
 9. Time Series Analysis
 10. Topic Modeling
 11. Recommendation Systems
 Index
Product Information
 Title: Mastering Predictive Analytics with R
 Author(s):
 Release date: June 2015
 Publisher(s): Packt Publishing
 ISBN: 9781783982806