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.
Handling Imbalanced Data Classification Problems
Taught in English
Instructor: Bhaskarjit Sarmah
1,884 already enrolled
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(55 reviews)
What you'll learn
Understand the business problem and the dataset to choose best evaluation metric for the problem
Create imbalanced data classification model using SMOTE data resampling technique
Compute to ROC curve and use to adjust probability threshold
Skills you'll practice
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Guided Project
(55 reviews)
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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:
Loading and understanding the dataset
Exploring the dataset
Evaluation metric selection
Creating a baseline model
Resampling techniques for imbalanced datasets
Implementing ROC curve
Adjusting probability threshold
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How you'll learn
Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
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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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