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

Issues with recommendation systems

Recommender engines are affected mainly by the following two issues:

  • The sparsity problem: Recommender engines work upon user preferences (or ratings for different items, depending upon the application) to predict or recommend products. Usually the ratings are given on some chosen scale but the user may choose not to rate certain items which he/she hasn't bought or looked at. For such cases, the rating is blank or zero. Hence, the ratings matrix R has elements of the form:

    For any real world application, such as an e-commerce platform, the size of such a ratings matrix is huge due to the large number of users and items available on the platform. Even though a lot of user related information is gathered on such a platform, the ratings matrix itself might still be pretty sparse, that is the matrix might have a many elements as blanks (or zeroes). This problem in general is termed the sparsity problem. The sparsity problem renders the recommender engine's predictions...