Handling Imbalanced Data

Summary

When one class vastly outnumbers another (e.g. 1% fraud cases) — plain accuracy becomes meaningless, and techniques like resampling, class weighting, or better metrics become necessary.

How to Learn This

1Find a public imbalanced dataset (fraud, churn) and check the class distribution.
2Learn why a model predicting 'always no fraud' can still show 99% accuracy.
3Research oversampling (SMOTE), undersampling, and class-weighting as mitigation strategies.

Dimensionality Reduction (PCA Overview)

Correlation Analysis

More topics in Exploratory Data Analysis & Feature Engineering

EDA Process & Techniques Handling Missing Data Outlier Detection & Treatment Feature Scaling (Normalization / Standardization)Encoding Categorical Variables Feature Creation & Selection Dimensionality Reduction (PCA Overview)Correlation Analysis

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