Credit Card Fraud Detection Using Deep Learning

This dataset includes 2034 credit card transactions, ideal for credit card fraud detection research and development using machine learning or deep learning. It contains 31 attributes: transaction time, transaction amount, 28 variables V1-V28 which are anonymized PCA-transformed, and a binary Class label with legitimate transactions (0) and fraudulent transactions (1). The dataset can be used for anomaly detection and classification, where models for fraud detection can be tested with the help of random forest, ANN, autoencoder, and hybrid deep learning models in imbalanced data analysis.