About This Book
- 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
If you are a data scientist, a data analyst, or an R and SPSS user with a good understanding of machine learning concepts, algorithms, and techniques, then this is the book for you. Some basic understanding of Spark and its core elements and application is required.
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
In Detail
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.
