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Why Handling Imbalanced Data is Important

Why Handling Imbalanced Data is Important

Why Handling Imbalanced Data is Important

Why Handling Imbalanced Data is Important

One of the biggest challenges in fraud detection is that fraudulent transactions are extremely rare compared to legitimate transactions.

For example:

Genuine Transactions → 284,315
Fraudulent Transactions → 492

If a model simply predicts every transaction as genuine, it would still achieve an accuracy of more than 99%, even though it completely fails to detect fraud.

This demonstrates why accuracy alone is not a reliable metric for fraud detection.

Instead, additional evaluation metrics such as:

Precision
Recall
F1-Score
ROC-AUC Score

are used to measure how effectively the model detects fraudulent transactions.

Understanding class imbalance is an essential step before training any fraud detection model.

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Why Handling Imbalanced Data is Important

Contents

Why Handling Imbalanced Data is Important

Credit Card Fraud Detection for Beginners using Data Science

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