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

Building a recommender engine

As discussed in the previous section, collaborative filtering is a simple yet very effective approach for predicting and recommending items to users. If we look closely, the algorithms work on input data, which is nothing but a matrix representation of the user ratings for different products.

Bringing in a mathematical perspective into the picture, matrix factorization is a technique to manipulate matrices and identify latent or hidden features from the data represented in the matrix. Building on the same concept, let us use matrix factorization as the basis for predicting ratings for items which the user has not yet rated.

Matrix factorization

Matrix factorization refers to the identification of two or more matrices such that when these matrices are multiplied we get the original matrix. Matrix factorization, as mentioned earlier, can be used to discover latent features between two different kinds of entities. We will understand and use the concepts of matrix...