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

Detecting and predicting trends

In this section, we will talk about what exactly we mean by trends and how the retailers detect and predict these trends. Basically, a trend in the retail context can be defined as a specific pattern or behavior which occurs over a period of time. This may involve a product or a combination of products being sold out in a very short period of time or even the reverse. A simple example would be a best-selling smartphone being prebooked and out of stock before even hitting the shelves on any e-commerce marketplace, or a combination of products like the classic beer and diapers combination which is frequently found in shopping baskets or carts of customers!

How can we even start analyzing shopping carts or start to detect and predict shopping trends. Like I mentioned earlier, we can achieve this with a combination of the right data and algorithms. Let's assume that we are heading a large retail chain. First we will have to keep track of each and every transaction...