University of North Texas
Predicting Credit Card Fraud with R
University of North Texas

Predicting Credit Card Fraud with R

Taught in English

John Garcia

Instructor: John Garcia

Included with Coursera Plus

Guided Project

Learn, practice, and apply job-ready skills with expert guidance

Intermediate level

Recommended experience

1.5 hours
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning
4.4

(32 reviews)

What you'll learn

  • 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.

Details to know

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Guided Project

Learn, practice, and apply job-ready skills with expert guidance

Intermediate level

Recommended experience

1.5 hours
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning
4.4

(32 reviews)

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Learn, practice, and apply job-ready skills in less than 2 hours

  • Receive training from industry experts
  • Gain hands-on experience solving real-world job tasks
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About this Guided Project

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  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.

Recommended experience

Familiarity with data analysis and using R.

5 project images

Instructor

Instructor ratings
4.7 (8 ratings)
John Garcia
University of North Texas
1 Course1,005 learners

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How you'll learn

  • Skill-based, hands-on learning

    Practice new skills by completing job-related tasks.

  • Expert guidance

    Follow along with pre-recorded videos from experts using a unique side-by-side interface.

  • No downloads or installation required

    Access the tools and resources you need in a pre-configured cloud workspace.

  • Available only on desktop

    This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.

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Learner reviews

Showing 3 of 32

4.4

32 reviews

  • 5 stars

    69.69%

  • 4 stars

    12.12%

  • 3 stars

    9.09%

  • 2 stars

    6.06%

  • 1 star

    3.03%

CS
5

Reviewed on Dec 8, 2021

JB
5

Reviewed on Apr 2, 2021

HR
5

Reviewed on Apr 18, 2024

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