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Predicting Credit Card Fraud with R(으)로 돌아가기

노스텍사스대학교의 Predicting Credit Card Fraud with R 학습자 리뷰 및 피드백

4.5
별점
27개의 평가

강좌 소개

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....

최상위 리뷰

JB

2021년 4월 2일

Very intriguing course and example application. Very informative and practical approaches to addressing imbalances in data. Excellent instructor and great course.

RV

2021년 2월 3일

It is best guided project which helps to learn caret library and this helped me to increase my r programming skills

필터링 기준:

Predicting Credit Card Fraud with R의 8개 리뷰 중 1~8

교육 기관: Vicente C K

2021년 5월 3일

교육 기관: James B

2021년 4월 2일

교육 기관: RASHIKA D

2020년 11월 12일

교육 기관: Ramachandra A V

2021년 2월 4일

교육 기관: Jason M

2021년 4월 7일

교육 기관: Charles S

2021년 12월 9일

교육 기관: Gary M

2021년 4월 8일

교육 기관: kuo j

2022년 3월 26일