How to Handle Highly Imbalanced Data in EDA for Better Analysis

Handling highly imbalanced data during Exploratory Data Analysis (EDA) is crucial for extracting meaningful insights and building robust machine learning models. Imbalanced datasets—where one class significantly outnumbers the others—can lead to misleading