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Machine Learning in Java

You're reading from   Machine Learning in Java Helpful techniques to design, build, and deploy powerful machine learning applications in Java

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788474399
Length 300 pages
Edition 2nd Edition
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Authors (2):
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Ashish Bhatia Ashish Bhatia
Author Profile Icon Ashish Bhatia
Ashish Bhatia
Bostjan Kaluza Bostjan Kaluza
Author Profile Icon Bostjan Kaluza
Bostjan Kaluza
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Toc

Table of Contents (13) Chapters Close

Preface 1. Applied Machine Learning Quick Start FREE CHAPTER 2. Java Libraries and Platforms for Machine Learning 3. Basic Algorithms - Classification, Regression, and Clustering 4. Customer Relationship Prediction with Ensembles 5. Affinity Analysis 6. Recommendation Engines with Apache Mahout 7. Fraud and Anomaly Detection 8. Image Recognition with Deeplearning4j 9. Activity Recognition with Mobile Phone Sensors 10. Text Mining with Mallet - Topic Modeling and Spam Detection 11. What Is Next? 12. Other Books You May Enjoy

Fraud detection in insurance claims

First, we'll take a look at suspicious behavior detection, where the goal is to learn about patterns of fraud, which corresponds to modeling known knowns.

Dataset

We'll work with a dataset describing insurance transactions, which is publicly available in the Oracle database online documentation at http://docs.oracle.com/cd/B28359_01/datamine.111/b28129/anomalies.htm.

The dataset describes insurance claims on vehicle incidents for an undisclosed insurance company. It contains 15,430 claims; each claim is comprised of 33 attributes, describing the following components:

  • Customer demographic details (Age, Sex, MartialStatus, and so on)
  • Purchased policy (PolicyType, VehicleCategory...
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