Predicting Credit Card Fraud with R
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.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
by JApr 6, 2021
Very thorough and enlightening demonstration of the classification on unbalanced data sets.
by GMApr 8, 2021
Very knowledgeable instructor. Excellent information.
by RVFeb 3, 2021
It is best guided project which helps to learn caret library and this helped me to increase my r programming skills
by JBApr 2, 2021
Very intriguing course and example application. Very informative and practical approaches to addressing imbalances in data. Excellent instructor and great course.