Book Image

R Machine Learning By Example

By : Raghav Bali
Book Image

R Machine Learning By Example

By: Raghav Bali

Overview of this book

Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems. This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems. You’ll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms. Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R.
Table of Contents (15 chapters)
R Machine Learning By Example
About the Authors
About the Reviewer


Social network analysis is one the trending topics in the world of data science. As we have seen throughout the chapter, these platforms not only provide us with ways to connect but they also present a unique opportunity to study human dynamics at a global scale. Through this chapter, we have learned some interesting techniques. We started off by understanding data mining in the social network context followed by the importance of visualizations. We focused on Twitter and understood different objects and APIs to manipulate them. We used various packages from R, such as TwitteR and TM, to connect, collect, and manipulate data for our analysis. We used data from Twitter to learn about frequency throughout. Finally, we presented some of the challenges posed by social networks words and associations, popular devices used by tweeple, hierarchical clustering and even touched upon topic modeling. We used ggplot2 and wordcloud to visualize our results to the data mining process in general...