Apache Spark Machine Learning Blueprints

Key Features

• Customize Apache Spark and R to fit your analytical needs in customer research, fraud detection, risk analytics, and recommendation engine development
• Develop a set of practical Machine Learning applications that can be implemented in real-life projects
• A comprehensive, project-based guide to improve and refine your predictive models for practical implementation

Book Description

There’s a reason why Apache Spark has become one of the most popular tools in Machine Learning – its ability to handle huge datasets at an impressive speed means you can be much more responsive to the data at your disposal. This book shows you Spark at its very best, demonstrating how to connect it with R and unlock maximum value not only from the tool but also from your data.

Packed with a range of project “blueprints” that demonstrate some of the most interesting challenges that Spark can help you tackle, you’ll find out how to use Spark notebooks and access, clean, and join different datasets before putting your knowledge into practice with some real-world projects, in which you will see how Spark Machine Learning can help you with everything from fraud detection to analyzing customer attrition. You’ll also find out how to build a recommendation engine using Spark’s parallel computing powers.

What you will learn

• Set up Apache Spark for machine learning and discover its impressive processing power
• Combine Spark and R to unlock detailed business insights essential for decision making
• Build machine learning systems with Spark that can detect fraud and analyze financial risks
• Build predictive models focusing on customer scoring and service ranking
• Build a recommendation systems using SPSS on Apache Spark
• Tackle parallel computing and find out how it can support your machine learning projects
• Turn open data and communication data into actionable insights by making use of various forms of machine learning

About the Author

Alex Liu is an expert in research methods and data science. He is currently one of IBM’s leading experts in Big Data analytics and also a lead data scientist, where he serves big corporations, develops Big Data analytics IPs, and speaks at industrial conferences such as STRATA, Insights, SMAC, and BigDataCamp. In the past, Alex served as chief or lead data scientist for a few companies, including Yapstone, RS, and TRG. Before this, he was a lead consultant and director at RMA, where he provided data analytics consultation and training to many well-known organizations, including the United Nations, Indymac, AOL, Ingram Micro, GEM, Farmers Insurance, Scripps Networks, Sears, and USAID. At the same time, he taught advanced research methods to PhD candidates at University of Southern California and University of California at Irvine. Before this, he worked as a managing director for CATE/GEC and as a research fellow for the Asia/Pacific Research Center at Stanford University. Alex has a Ph.D. in quantitative sociology and a master’s degree of science in statistical computing from Stanford University.

Table of Contents

Chapter 1. Spark for Machine Learning
Chapter 2. Data Preparation for Spark ML
Chapter 3. A Holistic View on Spark
Chapter 4. Fraud Detection on Spark
Chapter 5. Risk Scoring on Spark
Chapter 6. Churn Prediction on Spark
Chapter 7. Recommendations on Spark
Chapter 8. Learning Analytics on Spark
Chapter 9. City Analytics on Spark
Chapter 10. Learning Telco Data on Spark
Chapter 11. Modeling Open Data on Spark