Episode 11 of The Data Science Podcast tackles the hidden danger of imbalanced datasets. Lucas and Luna walk through a real-world example: a fraud detection model trained on 99.9 percent legitimate transactions and 0.1 percent frauds. The model achieved 99.9 percent accuracy yet caught zero frauds. They explain why accuracy is a terrible metric on imbalanced data, introduce precision-recall curves and F1-score as better alternatives, and discuss resampling techniques like SMOTE and cost-sensitive learning. Listeners will learn how to spot imbalance traps in their own projects and why some problems require rethinking the loss function entirely. The conversation stays practical and code-adjacent without getting lost in syntax. If you have ever trained a classifier on skewed data and felt something was off, this episode will give you the diagnostic tools to fix it.

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