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R Machine Learning Projects

You're reading from R Machine Learning Projects Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5

Product type Paperback

Published in Jan 2019

Publisher Packt

ISBN-13 9781789807943

Length 334 pages

Edition 1st Edition

Languages

R

Tools

H2O

Concepts

Machine Learning

Author (1):

Dr. Sunil Kumar Chinnamgari

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Table of Contents (12) Chapters

Preface

1. Exploring the Machine Learning Landscape FREE CHAPTER

2. Predicting Employee Attrition Using Ensemble Models

3. Implementing a Jokes Recommendation Engine

4. Sentiment Analysis of Amazon Reviews with NLP

5. Customer Segmentation Using Wholesale Data

6. Image Recognition Using Deep Neural Networks

7. Credit Card Fraud Detection Using Autoencoders

8. Automatic Prose Generation with Recurrent Neural Networks

9. Winning the Casino Slot Machines with Reinforcement Learning

10. The Road Ahead

11. Other Books You May Enjoy

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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...

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Authors (1)

Dr. Sunil Kumar Chinnamgari

Dr. Sunil Kumar Chinnamgari

Dr. Sunil Kumar Chinnamgari has a Ph.D. in computer science and specializes in machine learning and natural language processing. He is an AI researcher with more than 14 years of industry experience. Currently, he works in the capacity of lead data scientist with a US financial giant. He has published several research papers in Scopus and IEEE journals and is a frequent speaker at various meetups. He is an avid coder and has won multiple hackathons. In his spare time, Sunil likes to teach, travel, and spend time with family.

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