In this project, I tackled the challenge of credit card fraud detection, an issue that affects financial institutions and consumers worldwide. The objective was to develop a robust machine learning system capable of identifying fraudulent transactions effectively. To to that, I employed three distinct models: logistic regression, random forest, and shallow neural networks. Each model was chosen for its unique strengths in handling binary classification problems and its potential to perform well on imbalanced datasets.
