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Python 3
Understanding Imbalanced Data

Understanding Imbalanced Data

Understanding Imbalanced Data

Understanding Imbalanced Data

One of the biggest challenges in fraud detection is class imbalance.

In the credit card dataset:

Genuine transactions represent almost 99.8% of the data.
Fraudulent transactions represent only 0.2%.

This imbalance can mislead a machine learning model.

For example, if a model predicts every transaction as genuine, it may still achieve very high accuracy while completely failing to detect fraudulent transactions.

Therefore, handling imbalanced data is a critical step before training the model.

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Understanding Imbalanced Data

Contents

Understanding Imbalanced Data

Credit Card Fraud Detection for Beginners using Data Science

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