Bali R. - R Machine Learning By Example

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!

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 .

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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.