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Scala Machine Learning Projects

You're reading from   Scala Machine Learning Projects Build real-world machine learning and deep learning projects with Scala

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788479042
Length 470 pages
Edition 1st Edition
Languages
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Author (1):
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Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Table of Contents (13) Chapters Close

Preface 1. Analyzing Insurance Severity Claims 2. Analyzing and Predicting Telecommunication Churn FREE CHAPTER 3. High Frequency Bitcoin Price Prediction from Historical and Live Data 4. Population-Scale Clustering and Ethnicity Prediction 5. Topic Modeling - A Better Insight into Large-Scale Texts 6. Developing Model-based Movie Recommendation Engines 7. Options Trading Using Q-learning and Scala Play Framework 8. Clients Subscription Assessment for Bank Telemarketing using Deep Neural Networks 9. Fraud Analytics Using Autoencoders and Anomaly Detection 10. Human Activity Recognition using Recurrent Neural Networks 11. Image Classification using Convolutional Neural Networks 12. Other Books You May Enjoy

Fraud Analytics Using Autoencoders and Anomaly Detection

Detecting and preventing fraud in financial companies, such as banks, insurance companies, and credit unions, is an important task in order to see a business grow. So far, in the previous chapter, we have seen how to use classical supervised machine learning models; now it's time to use other, unsupervised learning algorithms, such as autoencoders.

In this chapter, we will use a dataset having more than 284,807 instances of credit card use and for each transaction, where only 0.172% transactions are fraudulent. So, this is highly imbalanced data. And hence it would make sense to use autoencoders to pre-train a classification model and apply an anomaly detection technique to predict possible fraudulent transactions; that is, we expect our fraud cases to be anomalies within the whole dataset.

In summary, we will learn...

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