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Apache Spark Machine Learning Blueprints

You're reading from   Apache Spark Machine Learning Blueprints Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide

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Product type Paperback
Published in May 2016
Publisher Packt
ISBN-13 9781785880391
Length 252 pages
Edition 1st Edition
Languages
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Author (1):
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Alex Liu Alex Liu
Author Profile Icon Alex Liu
Alex Liu
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Table of Contents (13) Chapters Close

Preface 1. Spark for Machine Learning FREE CHAPTER 2. Data Preparation 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 Data on Spark 11. Modeling Open Data on Spark Index

Model estimation


For this project, our strategy for model estimation is to employ a complete SPSS Modeler stream developed from previous sections and then use SPSS Analytics Server for Spark Implementation. Our stream consists of SPSS Modeler Nodes for data treatment, as described in section, Data treatment and also model training Nodes with MLlib codes described in section, Methods for recommendation development as we prepared our SPSS Modeler to be ready to use MLlib in section, Spark for a recommendation engine.

As noted before, our IBM SPSS Modeler nodes created from Custom Dialog Builder depend on the Spark environment and will only run against IBM SPSS Analytic Server. SPSS Analytics Server is a tool to manage all the computing for model estimations and we have to employ IBM SPSS Analytic Server to implement the model estimation for this project, which makes everything easy for us. However, we also need to arrange for SPSS on the Spark system to run models for each movie category and...

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