Chapter 12: Data Preparation for Fraud Analytics: Final Capstone: Credit Card Fraud

In this project, we will analyze a credit card transaction dataset and apply machine learning techniques to detect fraudulent transactions. Credit card fraud is a pervasive issue that impacts both cardholders and financial institutions. Swift detection of fraudulent transactions can mitigate financial losses and bolster customer confidence. The dataset, named "creditcardfraud.csv", is an extensive collection of credit card transactions. It contains 5,050 transactions, of which 50 are fraudulent and 5,000 are legitimate. Each transaction is characterized by 30 features (V1–V28, Amount, and Class), where V1–V28 are anonymized features derived from a PCA transformation, 'Amount' represents the transaction amount, and 'Class' indicates if the transaction is fraudulent (1) or not (0).