Kredi kartı dolandırıcılık tespiti için dört sınıflandırma yöntemi test edilmiştir: (Naive Bayesian, destek vektör makinesi, K-en yakın komşu ve rastgele orman)

Banks suffer multimillion money losses each year for several reasons, the most important of which is due to credit card fraud. In actuality, the issue is how to cope the challenges we face with this kind of fraud. Skewed "class imbalance" is a very important challenge with regard to this kind of fraud. Therefore, in this study, we explore four data mining techniques, namely 'naïve Bayesian (NB)', 'Support Vector Machine (SVM)', 'K-Nearest Neighbor (KNN)' and Random 'Forest (RF)', on actual credit card transactions from European cardholders. This paper offers four major contributions. First, we used under-sampling to balance the dataset because of the high imbalance class, implying skewed distribution. Second, we applied well-known models (NB, SVM, KNN and RF) to our under-sampled class to classify the transactions into fraudulent and genuine followed by testing the performance measures using a "confusion matrix" and comparing them. Third, we adopted cross validation (CV) with 10 folds to test the accuracy of our models with a standard deviation followed by comparing the results for all our models. Next, we examined four models against the entire dataset (skewed) using the confusion matrix and AUC ('Area Under the ROC Curve') ranking measure in order to conclude the final results to determine which would be the best model for us to use with a particular type of fraud. In our work, is used the Python programming language. The results showing the best accuracy for the NB, SVM, KNN and RF classifiers are 97.46%, 95.04%, 97.55% and 97.7%, respectively. The comparative results display that RF performs better than NB, SVM and KNN, and the results, when utilized our proposed study on the entire dataset ('skewed'), achieved preferable outcomes than the undersampled dataset.