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


In this chapter, we covered a lot of ground! We started with a discussion about how trends are detected and predicted in the retail vertical. Then we dived into what market basket analysis really means and the core concepts, mathematical formulae underlying the algorithms, and the critical metrics which are used to evaluate the results obtained from the algorithms, notably, support, confidence, and lift. We also discussed the most popular techniques used for analysis, including contingency matrix evaluation, frequent itemset generation, and association rule mining. Next, we talked about how to make data driven decisions using market basket analysis. Finally, we implemented our own algorithms and also used some of the popular libraries in R, such as arules, to apply these techniques to some real world transactional data for detecting, predicting, and visualizing trends. Do note that these machine learning techniques only talk about product based recommendations purely based on purchase...