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Handling Imbalanced Data Classification Problems

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  • Project Overview
    • Welcome to this project-based course on Handling Imbalanced Data Classification Problems. In this project, you will learn how to apply various data resampling techniques like undersampling, oversampling, SMOTE on the imbalanced datasets and be able to build a classifier to identify or predict the minority class samples. By the end of this 2-hour long project, you will be able to understand what imbalanced datasets are, what are the evaluation metrics that we should consider while building imbalanced data classification models. You will build predictive models after resampling to balance the classes of target variables and use ROC curve to adjust probability threshold which will help you to improve the evaluation metric of your choice.