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Python 3
Feature Scaling

Feature Scaling

Feature Scaling

Feature Scaling

Machine learning algorithms often perform better when numerical features are on a similar scale.

In the credit card dataset, most variables have already been transformed using Principal Component Analysis (PCA). However, columns such as Amount and Time may still require scaling.

Feature scaling helps:

Improve model convergence
Prevent large values from dominating smaller ones
Increase training efficiency

Common scaling techniques include:

Standardization
Min-Max Scaling

In this project, feature scaling ensures that all input variables contribute appropriately during model training.

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Feature Scaling

Contents

Feature Scaling

Credit Card Fraud Detection for Beginners using Data Science

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