This is the code repository for Machine Learning with R Cookbook-Second Edition, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier.
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter 03.
The code will look like the following:
> install.packages("forecast")
> require(forecast)
> forecast(my_series, 4)
All the examples cover in this book have been tested on R version 3.4.1 and R studio version 1.0.153. Works on all OS.
The code files for chapters 01 to 12 are given. For chapter13 and 14, refer the chapter for code testing.