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

Frequent itemset generation

We will now look at a better technique to find patterns and detect frequently bought products. For this, we will be using the frequent itemset generation technique. We will be implementing this algorithm from scratch because, even though when we solve any machine learning or optimization problem we usually use readymade machine learning algorithms out of the box which are optimized and available in various R packages, one of the main objectives of this book is to make sure we understand what exactly goes on behind the scenes of a machine learning algorithm. Thus, we will see how we can build some of these algorithms ourselves using the principles of mathematics, statistics, and logic.

Getting started

The data we will be using for this is the shopping_transaction_log.csv dataset which we used to explain the concepts of market basket analysis at the beginning of the chapter. The code we will be using for this section is available in the ch3_frequent itemset generation...