Book Image

Machine Learning with R Cookbook, Second Edition - Second Edition

By : Yu-Wei, Chiu (David Chiu)
Book Image

Machine Learning with R Cookbook, Second Edition - Second Edition

By: Yu-Wei, Chiu (David Chiu)

Overview of this book

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.
Table of Contents (21 chapters)
Title Page
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Studying a case of linear regression on SLID data


To summarize the contents of the previous section, we will explore more complex data with linear regression. In this recipe, we will demonstrate how to apply linear regression to analyze the Survey of Labor and Income Dynamics (SLID) dataset.

Getting ready

Check whether the car library is installed and loaded, as it is required to access the dataset SLID.

How to do it...

Follow these steps to perform linear regression on SLID data:

  1. You can use the str function to get an overview of the data:
       > str(SLID)
        Output:
        'data.frame': 7425 obs. of 5 variables:
         $ wages : num 10.6 11 NA 17.8 NA ...
         $ education: num 15 13.2 16 14 8 16 12 14.5 15 10 ...
         $ age : int 40 19 49 46 71 50 70 42 31 56 ...
         $ sex : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 1 1
         1 2 1 ...
         $ language : Factor w/ 3 levels "English","French",..: 1 1 3 3
         1 1 1 1 1 1 ..  
  1. First, we visualize the variable...