This is the code repository for Mastering Predictive Analytics with R - Second Edition, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

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 real-world 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.

All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.

All chapters contains code files except chapter 14th.

The code will look like the following:

  /**
  * {@inheritdoc}
  */
  public function alterRoutes(RouteCollection $collection) {
if ($route = $collection->get('mymodule.mypage)) {
    $route->setPath('/my-page');
  }
 }

In order to work with and to run the code examples found in this book, the following should be noted:

• Bullet list R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows, and MacOS. To download R, there are a variety of locations available, including https://www.rstudio.com/products/rstudio/download.
• Bullet list R includes extensive accommodations for accessing documentation and searching for help. A good source of information is at http://www.rproject.org/help.html.
• Bullet list The capabilities of R are extended through user-created packages. Various packages are referred to and used throughout this book and the features of and access to each will be detailed as they are introduced. For example, the wordcloud package is introduced in Chapter 11, Topic Modeling to plot a cloud of words shared across documents. This is found at https://cran.rproject.org/web/packages/wordcloud/index.html.

• Mastering Predictive Analytics with R

• Mastering Predictive Analytics with Python

• Learning Predictive Analytics with R