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Python 3
Understanding the Credit Card Transactions Dataset

Understanding the Credit Card Transactions Dataset

Understanding the Credit Card Transactions Dataset

Understanding the Credit Card Transactions Dataset

The dataset used in this project contains historical credit card transaction records collected from European cardholders. It includes both genuine and fraudulent transactions, making it suitable for binary classification tasks.

To protect customer privacy, most transaction features have been transformed using Principal Component Analysis (PCA) and are represented as anonymized variables.

Some important columns include:

Column	Description
Time	Time elapsed since the first recorded transaction
V1 – V28	Anonymized numerical features generated using PCA
Amount	Monetary value of the transaction
Class	Target variable (0 = Genuine, 1 = Fraudulent)

The Class column is the prediction target used to train the classification model.

One of the main challenges of this dataset is class imbalance, as fraudulent transactions account for only a very small percentage of the total records.

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Understanding the Credit Card Transactions Dataset

Contents

Understanding the Credit Card Transactions Dataset

Credit Card Fraud Detection for Beginners using Data Science

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