Effectively Dealing with Imbalance Classes

Offered By
Coursera Project Network
In this Guided Project, you will:

Import dataset and perform EDA & visualizations

Become familiar with the variety of under sampling techniques, their advantages & dis-advantages and implement them.

Clock2 Hours
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this 2 hour guided project you will learn how to deal with imbalance classification problems in a profound manner, applying several resampling strategies and visualizing the effects of resampling on imbalance classification dataset. Note: This project works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

ADASYNSMOTETomekSMOTEMachine LearningData Visualization (DataViz)

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: Importing data, Exploratory data analysis & visualizations

  2. Task 2: Applying under sampling strategies: Random & TomekLinks

  3. Task 3: Applying over sampling strategies: SMOTE & SVMSMOTE

  4. Task 4: Combining Over & Under Sampling strategies: SMOTETomek

  5. Task 5: Metrics Discussion & Comparison of impact of all the strategies

How Guided Projects work

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

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.