Apache Spark Machine Learning Blueprints

Summary

Deployment

Results explanation

Model estimation

Data and feature preparation

Spark for learning from open data

Chapter 11. Modeling Open Data on Spark

Summary

Model deployment

Results explanation

Model evaluation

Model estimation

Data and feature development

Methods for learning from Telco Data

Spark for using Telco Data

Chapter 10. Learning Telco Data on Spark

Summary

Explanations of the results

Model evaluation

Model estimation

Data and feature preparation

Spark for service forecasting

Chapter 9. City Analytics on Spark

Summary

Deployment

Results explanation

Model evaluation

Model estimation

Feature preparation

Methods of attrition prediction

Spark for attrition prediction

Chapter 8. Learning Analytics on Spark

Summary

Recommendation deployment

Model evaluation

Model estimation

Data treatment with SPSS

Methods for recommendation

Apache Spark for a recommendation engine

Chapter 7. Recommendations on Spark

Summary

Deployment

Results explanation

Model evaluation

Model estimation

Feature preparation

Methods for churn prediction

Spark for churn prediction

Chapter 6. Churn Prediction on Spark

Summary

Deployment

Results explanation

Model evaluation

Model estimation

Data and feature preparation

Methods of risk scoring

Spark for risk scoring

Chapter 5. Risk Scoring on Spark

Summary

Deploying fraud detection

Results explanation

Model evaluation

Model estimation

Feature preparation

Methods for fraud detection

Spark for fraud detection

Chapter 4. Fraud Detection on Spark

Summary

Deployment

Results explanation

Model evaluation

Model estimation

Feature preparation

Methods for a holistic view

Spark for a holistic view

Chapter 3. A Holistic View on Spark

Summary

Repeatability and automation

Feature extraction

Dataset joining

Dataset reorganizing

Identity matching

Data cleaning

Accessing and loading datasets

Chapter 2. Data Preparation for Spark ML

Summary

Spark notebooks

ML workflow examples

ML workflows and Spark pipelines

Spark RDD and dataframes

MLlib

Machine learning algorithms

Spark computing for machine learning

Spark overview and Spark advantages

Chapter 1. Spark for Machine Learning

Customer support

Reader feedback

Conventions

Who this book is for

What you need for this book

What this book covers

Preface

eBooks discount offers and more

www.PacktPub.com

About the Reviewer

About the Author

Credits

Apache Spark Machine Learning Blueprints

coverpage

coverpage

Apache Spark Machine Learning Blueprints

Credits

About the Author

About the Reviewer

www.PacktPub.com

eBooks discount offers and more

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

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