Chevron Left
Back to Predicting Credit Card Fraud with R

Learner Reviews & Feedback for Predicting Credit Card Fraud with R by University of North Texas

About the Course

Welcome to Predicting Credit Card Fraud with R. In this project-based course, you will learn how to use R to identify fraudulent credit card transactions with a variety of classification methods and use R to generate synthetic samples to address the common problem of classification bias for highly imbalanced datasets—the class of interest (fraud) represents less than 1% of the observations. Class imbalance can make it difficult to detect the effect independent variables have on fraud, ultimately leading to higher misclassification rates. Fixing the imbalance allows the minority class (fraud) to be better learned by the classifier algorithms. After completing the project, you will be able to apply the methods introduced in the project to a wide range of classification problems that typically confront class imbalance, including predicting loan default, customer churn, cancer diagnosis, early high school dropout risk, and malware detection. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions....
Filter by:

1 - 1 of 1 Reviews for Predicting Credit Card Fraud with R


Nov 12, 2020

Great course for learning data imbalance techniques and ML algorithms in R!

Although I wish they would allow us to write the last 3 ggplot and data frame codes (where we had to compare the precision, recall and F1 scores through a graph) by ourselves instead of just running the pre-written codes, it was truly a wholesome experience.