Predicting Credit Card Fraud with R

4.9
별점
11개의 평가
제공자:
노스텍사스대학교
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

Use R to identify fraudulent credit card transactions with a variety of classification methods.

Create, train, and evaluate decision tree, naïve Bayes, and Linear discriminant analysis classification models using R

Generate synthetic samples to improve the performance of your models.

Clock1.5 hours
Intermediate중급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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.

개발할 기술

Data AnalysisMachine LearningR Programming

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  1. Task 1: Explore why imbalanced datasets are problematic for classification algorithms.

  2. Task 2: Use R to explore a dataset.

  3. Task 3: Create random testing and training datasets using the caret package in R.

  4. Task 4: Use R to synthetically balance your training dataset using three techniques from the smotefamily package.

  5. Task 5: Train three classification algorithms (decision tree, naïve Bayes, and linear discriminant analysis) using the natively imbalanced dataset, and generate the predictions for the test dataset.

  6. Task 6: Use R to visually compare your models using the recall, precision, and F measure classification accuracy metrics.

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

검토

PREDICTING CREDIT CARD FRAUD WITH R의 최상위 리뷰

모든 리뷰 보기

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