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

  • ADASYN
  • SMOTETomek
  • SMOTE
  • Machine Learning
  • Data 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.