Key Features

• Customize Apache Spark and R to suit your analytical wishes in shopper learn, fraud detection, possibility analytics, and advice engine development
• Develop a collection of functional computer studying functions that may be applied in real-life projects
• A accomplished, project-based consultant to enhance and refine your predictive versions for useful implementation

Book Description

There's a the reason is, Apache Spark has develop into some of the most well known instruments in laptop studying – its skill to deal with large datasets at a powerful velocity ability you will be even more aware of the knowledge at your disposal. This ebook exhibits you Spark at its best possible, demonstrating the way to attach it with R and free up greatest worth not just from the device but in addition out of your data.

Packed with a variety of undertaking "blueprints" that exhibit one of the most attention-grabbing demanding situations that Spark will help take on, you will discover out find out how to use Spark notebooks and entry, fresh, and subscribe to assorted datasets prior to placing your wisdom into perform with a few real-world tasks, during which one can find how Spark computer studying might actually help with every thing from fraud detection to studying patron attrition. you will additionally tips to construct a advice engine utilizing Spark's parallel computing powers.

What you are going to learn

• Set up Apache Spark for computing device studying and detect its extraordinary processing power
• Combine Spark and R to release unique company insights crucial for choice making
• Build computer studying structures with Spark that may discover fraud and learn monetary risks
• Build predictive types concentrating on client scoring and repair ranking
• Build a suggestion platforms utilizing SPSS on Apache Spark
• Tackle parallel computing and learn the way it may possibly help your computing device studying projects
• Turn open facts and verbal exchange facts into actionable insights by way of utilizing a number of varieties of computing device learning

About the Author

Alex Liu is knowledgeable in examine equipment and information technology. he's at present one in all IBM's major specialists in vast info analytics and in addition a lead information scientist, the place he serves great enterprises, develops enormous facts analytics IPs, and speaks at commercial meetings akin to STRATA, Insights, SMAC, and BigDataCamp. some time past, Alex served as leader or lead facts scientist for a couple of businesses, together with Yapstone, RS, and TRG. earlier than this, he used to be a lead advisor and director at RMA, the place he supplied info analytics session and coaching to many famous enterprises, together with the United countries, Indymac, AOL, Ingram Micro, GEM, Farmers coverage, Scripps Networks, Sears, and USAID. even as, he taught complex study easy methods to PhD applicants at collage of Southern California and college of California at Irvine. prior to this, he labored as a handling director for CATE/GEC and as a learn fellow for the Asia/Pacific study heart at Stanford college. Alex has a Ph.D. in quantitative sociology and a master's measure of technological know-how in statistical computing from Stanford University.

Table of Contents

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