Book description
Master the craft of predictive modeling in R by developing strategy, intuition, and a solid foundation in essential concepts
About This Book
 Grasping the major methods of predictive modeling and moving beyond black box thinking to a deeper level of understanding
 Leveraging the flexibility and modularity of R to experiment with a range of different techniques and data types
 Packed with practical advice and tips explaining important concepts and best practices to help you understand quickly and easily
Who This Book Is For
Although budding data scientists, predictive modelers, or quantitative analysts with only basic exposure to R and statistics will find this book to be useful, the experienced data scientist professional wishing to attain master level status , will also find this book extremely valuable.. This book assumes familiarity with the fundamentals of R, such as the main data types, simple functions, and how to move data around. Although no prior experience with machine learning or predictive modeling is required, there are some advanced topics provided that will require more than novice exposure.
What You Will Learn
 Master the steps involved in the predictive modeling process
 Grow your expertise in using R and its diverse range of packages
 Learn how to classify predictive models and distinguish which models are suitable for a particular problem
 Understand steps for tidying data and improving the performing metrics
 Recognize the assumptions, strengths, and weaknesses of a predictive model
 Understand how and why each predictive model works in R
 Select appropriate metrics to assess the performance of different types of predictive model
 Explore word embedding and recurrent neural networks in R
 Train models in R that can work on very large datasets
In Detail
R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems.
The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. 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 realworld datasets. How do you train models that can handle really large datasets? This book will also show you just that. Finally, you will tackle the really important topic of deep learning by implementing applications on word embedding and recurrent neural networks.
By the end of this book, you will have explored and tested the most popular modeling techniques in use on real world datasets and mastered a diverse range of techniques in predictive analytics using R.
Style and approach
This book takes a stepbystep approach in explaining the intermediate to advanced concepts in predictive analytics. Every concept is explained in depth, supplemented with practical examples applicable in a realworld setting.
Publisher resources
Table of contents

Mastering Predictive Analytics with R Second Edition
 Table of Contents
 Mastering Predictive Analytics with R Second Edition
 Credits
 About the Authors
 About the Reviewer
 www.PacktPub.com
 Customer Feedback
 Preface

1. Gearing Up for Predictive Modeling
 Models
 Types of model
 The process of predictive modeling
 Summary
 2. Tidying Data and Measuring Performance
 3. Linear Regression
 4. Generalized Linear Models
 5. Neural Networks
 6. Support Vector Machines
 7. TreeBased Methods

8. Dimensionality Reduction

Defining DR
 Correlated data analyses
 Scatterplots
 Causation
 The degree of correlation
 Reporting on correlation
 Principal component analysis
 Using R to understand PCA
 Independent component analysis
 Defining independence
 ICA preprocessing
 Factor analysis
 Explore and confirm
 Using R for factor analysis
 The output
 NNMF
 Summary

Defining DR
 9. Ensemble Methods
 10. Probabilistic Graphical Models
 11. Topic Modeling
 12. Recommendation Systems
 13. Scaling Up
 14. Deep Learning
 Index
Product information
 Title: Mastering Predictive Analytics with R  Second Edition
 Author(s):
 Release date: August 2017
 Publisher(s): Packt Publishing
 ISBN: 9781787121393
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