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Apache Spark Machine Learning Blueprints

By : Alex Liu

1 (1)

Apache Spark Machine Learning Blueprints

1 (1)

By: Alex Liu

Overview of this book

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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Spark for Machine Learning

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

2. Data Preparation for Spark ML

2. Data Preparation for Spark ML

Accessing and loading datasets

Data cleaning

Identity matching

Dataset reorganizing

Dataset joining

Feature extraction

Repeatability and automation

Summary

3. A Holistic View on Spark

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

4. Fraud Detection on Spark

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

5. Risk Scoring on Spark

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

6. Churn Prediction on Spark

6. Churn Prediction on Spark

Spark for churn prediction

Methods for churn prediction

Feature preparation

Model estimation

Model evaluation

Results explanation

Deployment

Summary

7. Recommendations on Spark

7. Recommendations on Spark

Apache Spark for a recommendation engine

Methods for recommendation

Data treatment with SPSS

Model estimation

Model evaluation

Recommendation deployment

Summary

8. Learning Analytics on Spark

8. Learning Analytics on Spark

Spark for attrition prediction

Methods of attrition prediction

Feature preparation

Model estimation

Model evaluation

Results explanation

Deployment

Summary

9. City Analytics on Spark

9. City Analytics on Spark

Spark for service forecasting

Data and feature preparation

Model estimation

Model evaluation

Explanations of the results

Summary

10. Learning Telco Data on Spark

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

11. Modeling Open Data on Spark

11. Modeling Open Data on Spark

Spark for learning from open data

Data and feature preparation

Model estimation

Results explanation

Deployment

Summary

Index

Index

Dataset joining

In this section, we will cover dataset joining techniques. We will also discuss some of Spark's special features for data joining plus some data joining solutions made easy with Spark.

After this section, we will be able to join data for various machine learning needs.

Dataset joining and its tool – the Spark SQL

In preparing datasets for a machine learning project, we often need to combine data from multiple datasets. For relational tables, the task is to join tables through a primary and foreign key relationship.

Joining two or more datasets together sounds easy, but can be very challenging and time consuming. In SQL, SELECT is the most frequently used command. As an example, the following is a typical SQL code to perform a join:

SELECT column1, column2, …
FROM table1, table2
WHERE table1.joincolumn = table2.joincolumn
AND search_condition(s);

To work with the table joining tasks mentioned before, data scientists and machine learning professionals often utilize...

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Tech Concepts

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Programming languages

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