This project is aimed at leveraging dataset containing > 500K credit card transaction in Europe in 2023 to train a ML model to predict/detect fraudulent transactions.
This project aims to leverage unsupervised machine learning to develop a high-performance model capable of detecting fraudulent activity within a dataset of over 550,000 discrete credit card transactions recorded across Europe in 2023. The primary objective is to deploy an algorithm that effectively identifies potential fraud within these transaction records. A secondary objective of the analysis is to gain an in-depth, analytical view of transaction patterns, specifically in terms of transaction amounts, across European users.