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


We explored several important areas in the world of supervised learning in this chapter. If you have followed this chapter from the beginning of our journey and braved your way till the end, give yourself a pat on the back! You now know what constitutes predictive analytics and some of the important concepts associated with it. Also, we have seen how predictive modeling works and the full predictive analytics pipeline in actual practice. This will enable you to build your own predictive models in the future and start deriving valuable insights from model predictions. We also saw how to actually use models to make predictions and evaluate these predictions to test model performance so that we can optimize the models further and then select the best model based on metrics as well and business requirements. Before we conclude and you start your own journey into predictive analytics, I will like to mention that you should always remember Occam's razor, which states that Among competing...