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

Twitter data mining

Now that we have tested our tools, libraries, and connections to Twitter APIs, the time has come to begin our search for the hidden treasures in Twitter land. Let's wear our data miner's cap and start digging!

In this section, we will be working on Twitter data gathered from searching keywords (or hashtags in Twitter vocabulary) and user timelines. Using this data, we will be uncovering some interesting insights while using different functions and utilities from TwitteR and other R packages.


Please note that our process will implicitly follow the steps outlined for data mining. In the spirit of brevity, we might take the liberty to not mention each of the steps explicitly. We are mining for some gold-plated insights; rest assured nothing is skipped!

Every year, we begin with a new zeal to achieve great feats and improve upon our shortcomings. Most of us make promises to ourselves in the form of New Year's resolutions. Let us explore what tweeple are doing with their...