Credit card Fraud detection is a critical task in various industries,
including finance and e-commerce, where identifying fraudulent
activities can help prevent financial losses and protect users. It begins
by combining two datasets containing fraudulent and non-fraudulent
transactions to create a comprehensive dataset for analysis. Data is
preprocessed by removing unnecessary features, calculating distance
metrics, and generating new variables to capture temporal patterns and
transaction history. Multicollinearity issues are addressed through
feature selection. Improved Random Forest (RF) algorithm is used to
improve fraud detection. The experimental results indicate that the
improved Random Forest algorithm achieves commendable accuracy
in fraud detection. The proposed model achieves 99.87% training
accuracy and 99.41% testing accuracy. The Model’s performance is
evaluated by measuring precision, recall, F1-score and support. Our
research emphasizes the importance of considering improved
algorithms to achieve better results. The findings provide valuable
insights for organizations aiming to enhance their fraud detection
capabil..