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 continued our pursuit of using machine learning in the field of e-commerce to enhance sales and overall user experience. The previous chapter had discussed recommendations based on transactional logs; in this chapter, we accounted for the human factor and looked into the recommendation engines based on user behavior.

We started off by understanding what recommendation systems and their classifications into user-based, content-based, and hybrid recommender systems. We touched on the problems associated with recommender engines in general. Then we dived deep into the specifics of collaborative filters and discussed the math around prediction and similarity measures. After getting our basics straight, we moved onto building a recommender engine of our own from scratch. We utilized matrix factorization to build a recommender engine step by step using a small dummy dataset. We then moved onto building a production ready recommender engine using R's popular library called...