Name Verification Model with Docker

This project focuses on verifying Arabic names using a model that classifies names as either real or fake with a remarkable accuracy of 99%. The approach involves utilizing word embeddings to represent names and distinguish between real and fabricated ones. To enhance the dataset, 30% of the actual data was shuffled to generate fake names for training and testing purposes. The final model was deployed using Flask and Docker, enabling easy integration and scalability.