4. Fraud Detection on Spark | Apache Spark Machine Learning Blueprints

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Table Of Contents

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

Model evaluation

In the last section, we completed our model estimation. Now, it is the time for us to evaluate these estimated models to see whether they fit our client's criteria so that we can either move to results explanation or go back to some previous stages to refine our predictive models.

To perform our model evaluation, in this section, we will focus on utilizing confusion matrix and FalsePositive numbers to assess the goodness of fit for our models. To calculate them, we need to use our test data rather than training data.

A quick evaluation

As discussed before, both MLlib and R have algorithms to return a confusion matrix and even false positive numbers.

MLlib has confusionMatrix and numFalseNegatives() to use.

The following code calculates error ratios:

// Evaluate model on test instances and compute test error
val testErr = testData.map { point =>
  val prediction = model.predict(point.features)
  if (point.label == prediction) 1.0 else 0.0
}.mean()
println("Test Error = " + testErr...

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Programming languages

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