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

Predictive analytics

We had already discussed a fair bit about predictive analytics in the previous chapter to give you a general overview of what it means. We will be discussing it in more detail in this section. Predictive analytics can be defined as a subset of the machine learning universe, which encompasses a wide variety of supervised learning algorithms based on data science, statistics, and mathematical formulae which enable us to build predictive models using these algorithms and data which has already been collected. These models enable us to make predictions of what might happen in the future based on past observations. Combining this with domain knowledge, expertise, and business logic enables analysts to make data driven decisions using these predictions, which is the ultimate outcome of predictive analytics.

The data we are talking about here is data which has already been observed in the past and has been collected over a period of time for analysis. This data is often known...