Book Image

Advanced Machine Learning with R

By : Cory Lesmeister, Dr. Sunil Kumar Chinnamgari
Book Image

Advanced Machine Learning with R

By: Cory Lesmeister, Dr. Sunil Kumar Chinnamgari

Overview of this book

R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics. This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You’ll work through realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. Next, you’ll explore different clustering techniques to segment customers using wholesale data and even apply TensorFlow and Keras-R for performing advanced computations. Each chapter will help you implement advanced machine learning algorithms using real-world examples. You’ll also be introduced to reinforcement learning along with its use cases and models. Finally, this Learning Path will provide you with a glimpse into how some of these black box models can be diagnosed and understood. By the end of this Learning Path, you’ll be equipped with the skills you need to deploy machine learning techniques in your own projects.
Table of Contents (30 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Chapter 20. Credit Card Fraud Detection Using Autoencoders

Fraud management has been known to be a very painful problem for banking and finance firms. Card-related frauds have proven to be especially difficult for firms to combat. Technologies such as chip and PIN are available and are already used by most credit card system vendors, such as Visa and MasterCard. However, the available technology is unable to curtail 100% of credit card fraud. Unfortunately, scammers come up with newer ways of phishing to obtain passwords from credit card users. Also, devices such as skimmers make stealing credit card data a cake walk!

Despite the availability of some technical abilities to combat credit card fraud, The Nilson Report, a leading publication covering payment systems worldwide, estimated that credit card fraud is going to soar to $32 billion in 2020 (https://nilsonreport.com/upload/content_promo/The_Nilson_Report_10-17-2017.pdf). To get a perspective on the estimated loss, it is more than the...