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Bali Raghav - R Machine Learning by Example

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Bali Raghav R Machine Learning by Example

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Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfullyAbout This Book Get to grips with the concepts of machine learning through exciting real-world examples Visualize and solve complex problems by using power-packed R constructs and its robust packages for machine learning Learn to build your own machine learning system with this example-based practical guide Who This Book Is ForIf you are interested in mining useful information from data using state-of-the-art techniques to make data-driven decisions, this is a go-to guide for you. No prior experience with data science is required, although basic knowledge of R is highly desirable. Prior knowledge in machine learning would be helpful but is not necessary.What You Will Learn Utilize the power of R to handle data extraction, manipulation, and exploration techniques Use R to visualize data spread across multiple dimensions and extract useful features Explore the underlying mathematical and logical concepts that drive machine learning algorithms Dive deep into the world of analytics to predict situations correctly Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action Write reusable code and build complete machine learning systems from the ground up Solve interesting real-world problems using machine learning and R as the journey unfolds Harness the power of robust and optimized R packages to work on projects that solve real-world problems in machine learning and data science In DetailData 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.Youll 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.Style and approachThe book is an enticing journey that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application.

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R Machine Learning By Example

R Machine Learning By Example

Copyright 2016 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: March 2016

Production reference: 1220316

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78439-084-6

www.packtpub.com

Credits

Authors

Raghav Bali

Dipanjan Sarkar

Reviewer

Alexey Grigorev

Commissioning Editor

Akram Hussain

Acquisition Editors

Kevin Colaco

Tushar Gupta

Content Development Editor

Kajal Thapar

Technical Editor

Utkarsha S. Kadam

Copy Editors

Vikrant Phadke

Alpha Singh

Project Coordinator

Shweta H Birwatkar

Proofreader

Safis Editing

Indexer

Monica Ajmera Mehta

Graphics

Disha Haria

Kirk D'Penha

Production Coordinator

Arvindkumar Gupta

Cover Work

Arvindkumar Gupta

About the Authors

Raghav Bali has a master's degree (gold medalist) in IT from the International Institute of Information Technology, Bangalore. He is an IT engineer at Intel, the world's largest silicon company, where he works on analytics, business intelligence, and application development. He has worked as an analyst and developer in domains such as ERP, finance, and BI with some of the top companies in the world. Raghav is a shutterbug, capturing moments when he isn't busy solving problems.

I would like to thank Packt Publishing for this opportunity, Kajal Thapar and Utkarsha S. Kadam for their fantastic support and editing, and everyone from the R community for making life simpler and data science interesting.

Finally, I would to thank my family, especially my parents and brother for their faith in me and for whom this book will be a surprise. I would also like to thank my mentors, teachers, and friends, who have always been an inspiration. Last but not least, special thanks to my partner in crime, Dipanjan Sarkar, without whom this wouldn't have been possible.

Dipanjan Sarkar is an IT engineer at Intel, the world's largest silicon company, where he works on analytics, business intelligence, and application development. He received his master's degree in information technology from the International Institute of Information Technology, Bangalore. His areas of specialization includes software engineering, data science, machine learning, and text analytics.

Dipanjan's interests include learning about new technology, disruptive start-ups, and data science. In his spare time, he loves reading, playing games, and watching popular sitcoms. He has also reviewed Data Analysis with R , Learning R for Geospatial Analysis , and R Data Analysis Cookbook , all by Packt Publishing.

I would like to thank my good friend and colleague, Raghav Bali, for co-authoring this book with me. Without his support, it would have been impossible to make this book a reality. I would also like to thank Kajal Thapar and Utkarsha S. Kadam for giving me timely feedback on the book's content and making the whole writing process really interactive and enjoyable. Much gratitude goes without saying to Packt Publishing for giving me this wonderful opportunity to share my knowledge with the machine learning and R enthusiasts out there who are doing truly amazing things every day.

Last but never the least, I am indebted to my family, friends, teachers, and colleagues for always standing by my side and supporting me in all my endeavors. Your support keeps me going day in, day out to take on new challenges!

About the Reviewer

Alexey Grigorev is a skilled data scientist and software engineer with more than 5 years of professional experience. He currently works as a data scientist at Searchmetrics. In his day-to-day job, he actively uses R and Python for data cleaning, data analysis, and modeling. He has been a reviewer on other Packt Publishing books on data analysis, such as Test-Driven Machine Learning and Mastering Data Analysis with R .

www.PacktPub.com
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Preface

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 make machine learning give them data-driven insights to grow their businesses. 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.

What this book covers

, Getting Started with R and Machine Learning , acquaints you with the book and helps you reacquaint yourself with R and its basics. This chapter also provides you with a short introduction to machine learning.

, Let's Help Machines Learn , dives into machine learning by explaining the concepts that form its base. You are also presented with various types of learning algorithms, along with some real-world examples.

, Predicting Customer Shopping Trends with Market Basket Analysis , starts off with our first project, e-commerce product recommendations, predictions, and pattern analysis, using various machine learning techniques. This chapter specifically deals with market basket analysis and association rule mining to detect customer shopping patterns and trends and make product predictions and suggestions using these techniques. These techniques are used widely by retail companies and e-commerce stores such as Target, Macy's, Flipkart, and Amazon for product recommendations.

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