March 2017
Beginner
284 pages
5h 32m
English
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Fraud detection
Identifying fraudulent transactions is one of the most important components of risk management. R has many functions and packages that can be used to find fraudulent transactions, including binary classification techniques such as logistic regression, decision tree, random forest, and so on. We will be again using a subset of the German Credit data available in R library. In this section, we are going to use random forest for fraud detection. Just like logistic regression, we can do basic exploratory analysis to understand the attributes. Here we are not going to do the basic exploratory analysis but will be using the labeled data to train the model using random forest, and then will try to do the prediction of fraud on validation ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.