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

Association rule mining

We will now be implementing the final technique in market basket analysis for finding out association rules between itemsets to detect and predict product purchase patterns which can be used for product recommendations and suggestions. We will be notably using the Apriori algorithm from the arules package which uses an implementation for generating frequent itemsets first, which we discussed earlier. Once it has the frequent itemsets, the algorithm generates necessary rules based on parameters such as support, confidence, and lift. We will also show how you can visualize and interact with these rules using the arulesViz package. The code for this implementation is in the ch3_association rule mining.R file which you can directly load and follow the book.

Loading dependencies and data

We will first load the necessary package and data dependencies. Do note that we will be using the Groceries dataset which we discussed earlier in the section dealing with advanced contingency...