Enhancing Credit Card Fraud Detection: A Machine Learning Framework

In the current digital age, credit card tracking is an essential component of financial security. The aim of this study is to use the 1,604,292 data in the Credit Card Transactions Prevention Dataset from Kaggle to create efficient fraud detection algorithms. 1,048,574 records in the dataset are put aside for training, and the remaining records are set aside for testing. Preprocessing techniques like one-hot encoding for classification parameters and label encoding for numbers are used to get the data ready for analysis. The preprocessed dataset is then used to train a variety of machine learning classifiers, such as Random Forest, Decision Tree, Gradient Boosting (GB), and Logistic Regression. The goal is to use binary target properties to identify fraudulent transactions. The Random Forest classifier obtains the highest accuracy, with an astonishing 99.47%, according to the results. Remarkably, the system exhibits infrequent occurrences of data loss, underscoring its resilience and efficiency in identifying fraudulent activity. This study emphasizes how important it is to fight credit card theft by using sophisticated machine-learning strategies and meticulous data pretreatment procedures. The results add to the continuing efforts to improve bank security and shield customers from fraudulent online transactions.