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Learner Reviews & Feedback for Handling Imbalanced Data Classification Problems by Coursera Project Network

4.9
stars
14 ratings
2 reviews

About the Course

In this 2-hour long project-based course on handling imbalanced data classification problems, you will learn to understand the business problem related we are trying to solve and and understand the dataset. You will also learn how to select best evaluation metric for imbalanced datasets and data resampling techniques like undersampling, oversampling and SMOTE before we use them for model building process. At the end of the course you will understand and learn how to implement ROC curve and adjust probability threshold to improve selected evaluation metric of the model. 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....
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1 - 2 of 2 Reviews for Handling Imbalanced Data Classification Problems

By Marwa A E

Aug 03, 2020

Introduced to me the concept of SMOTE and how to use it for imbalanced datasets. Seeing, the effect of it on the datasets manipulated predicted results also showed how this technique makes classification problems more accurate.

By Jesus M Z F

Aug 01, 2020

Great course