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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Apache Spark Machine Learning Blueprints

Alex Liu

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Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide
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Book Details

ISBN 139781785880391
Paperback252 pages

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.

Table of Contents

Chapter 1: Spark for Machine Learning
Spark overview and Spark advantages
Spark computing for machine learning
Machine learning algorithms
MLlib
Spark RDD and dataframes
ML workflows and Spark pipelines
ML workflow examples
Spark notebooks
Summary
Chapter 2: Data Preparation for Spark ML
Accessing and loading datasets
Data cleaning
Identity matching
Dataset reorganizing
Dataset joining
Feature extraction
Repeatability and automation
Summary
Chapter 3: A Holistic View on Spark
Spark for a holistic view
Methods for a holistic view
Feature preparation
Model estimation
Model evaluation
Results explanation
Deployment
Summary
Chapter 4: Fraud Detection on Spark
Spark for fraud detection
Methods for fraud detection
Feature preparation
Model estimation
Model evaluation
Results explanation
Deploying fraud detection
Summary
Chapter 5: Risk Scoring on Spark
Spark for risk scoring
Methods of risk scoring
Data and feature preparation
Model estimation
Model evaluation
Results explanation
Deployment
Summary
Chapter 6: Churn Prediction on Spark
Spark for churn prediction
Methods for churn prediction
Feature preparation
Model estimation
Model evaluation
Results explanation
Deployment
Summary
Chapter 7: Recommendations on Spark
Apache Spark for a recommendation engine
Methods for recommendation
Data treatment with SPSS
Model estimation
Model evaluation
Recommendation deployment
Summary
Chapter 8: Learning Analytics on Spark
Spark for attrition prediction
Methods of attrition prediction
Feature preparation
Model estimation
Model evaluation
Results explanation
Deployment
Summary
Chapter 9: City Analytics on Spark
Spark for service forecasting
Data and feature preparation
Model estimation
Model evaluation
Explanations of the results
Summary
Chapter 10: Learning Telco Data on Spark
Spark for using Telco Data
Methods for learning from Telco Data
Data and feature development
Model estimation
Model evaluation
Results explanation
Model deployment
Summary
Chapter 11: Modeling Open Data on Spark
Spark for learning from open data
Data and feature preparation
Model estimation
Results explanation
Deployment
Summary

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

Authors

Table of Contents

Chapter 1: Spark for Machine Learning
Spark overview and Spark advantages
Spark computing for machine learning
Machine learning algorithms
MLlib
Spark RDD and dataframes
ML workflows and Spark pipelines
ML workflow examples
Spark notebooks
Summary
Chapter 2: Data Preparation for Spark ML
Accessing and loading datasets
Data cleaning
Identity matching
Dataset reorganizing
Dataset joining
Feature extraction
Repeatability and automation
Summary
Chapter 3: A Holistic View on Spark
Spark for a holistic view
Methods for a holistic view
Feature preparation
Model estimation
Model evaluation
Results explanation
Deployment
Summary
Chapter 4: Fraud Detection on Spark
Spark for fraud detection
Methods for fraud detection
Feature preparation
Model estimation
Model evaluation
Results explanation
Deploying fraud detection
Summary
Chapter 5: Risk Scoring on Spark
Spark for risk scoring
Methods of risk scoring
Data and feature preparation
Model estimation
Model evaluation
Results explanation
Deployment
Summary
Chapter 6: Churn Prediction on Spark
Spark for churn prediction
Methods for churn prediction
Feature preparation
Model estimation
Model evaluation
Results explanation
Deployment
Summary
Chapter 7: Recommendations on Spark
Apache Spark for a recommendation engine
Methods for recommendation
Data treatment with SPSS
Model estimation
Model evaluation
Recommendation deployment
Summary
Chapter 8: Learning Analytics on Spark
Spark for attrition prediction
Methods of attrition prediction
Feature preparation
Model estimation
Model evaluation
Results explanation
Deployment
Summary
Chapter 9: City Analytics on Spark
Spark for service forecasting
Data and feature preparation
Model estimation
Model evaluation
Explanations of the results
Summary
Chapter 10: Learning Telco Data on Spark
Spark for using Telco Data
Methods for learning from Telco Data
Data and feature development
Model estimation
Model evaluation
Results explanation
Model deployment
Summary
Chapter 11: Modeling Open Data on Spark
Spark for learning from open data
Data and feature preparation
Model estimation
Results explanation
Deployment
Summary

Book Details

ISBN 139781785880391
Paperback252 pages
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From 1 reviews

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