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

Challenges with social network data mining

Before we close the chapter, let us look at the different challenges posed by social networks to the process of data mining. The following points present a few arguments, questions, and challenges:

  • No doubt the data generated by social networks classifies as big data in every aspect. It has all the volume, velocity, and variety in it to overwhelm any system. Yet, interestingly, the challenge with such a huge source of data is the availability of enough granular data. If we zoom into our data sets and try to use data on a per user basis, we find that there isn't enough data to do some of the most common tasks, such as making recommendations!

  • Social networks such as Twitter handle millions of users creating and sharing tons of data every second. To keep their systems up and running at all times, they put limits upon the amount of data that can be tapped using their APIs (security is also a major reason behind these limits, though). These limits put...