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 is a goldmine for data science, with interesting patterns and insights spread all across it. Its constant flow of user-generated content, coupled with unique, interest-based relationships, present opportunities to understand human dynamics up close. Sentiments Analysis is one such field where Twitter provides the right set of ingredients to understand what and how we present and share opinions about products, brands, people, and so on.

Throughout this chapter, we have looked at the basics of Sentiment Analysis, key terms, and areas of application. We have also looked into the various challenges posed while performing sentiment analysis. We have looked at various commonly-used feature extraction methods such as tf-idf, Ngrams, POS, negation, and so on for performing sentiment analysis (or textual analysis in general). We have built on our code base from the previous chapter to streamline and structure utility functions for reuse. We have performed polarity analysis using Twitter...