Credit Card fraud detection using machine learning

Credit card fraud is among the most prevalent types of financial crimes today. With the increasing adoption of online payment systems by companies, the risk of fraudulent activities has also grown. Cybercriminals have developed various techniques to exploit online transactions and steal money. The primary goal of this study is to utilize various machine learning algorithms to distinguish between legitimate and fraudulent transactions. To achieve this, the transactions will be categorized into groups, allowing different machine learning models to be applied accordingly. Each group will be used to train different classifiers independently, and the model with the highest accuracy will be selected for fraud detection. This research uses a dataset comprising credit card transactions made by anonymous users. The dataset is highly imbalanced, containing a significantly higher number of genuine transactions compared to fraudulent ones.