Overview: This project focuses on detecting fraudulent credit card transactions using advanced machine learning techniques. Leveraging a neural network with entity embeddings for categorical variables, the system was designed to handle an imbalanced dataset containing over 1.5 million transactions from 953 unique customers. The objective was to improve fraud detection accuracy while minimizing false positives, addressing the critical challenge of the imbalanced nature of fraudulent transactions (less than 0.1%).