The project is mainly focussed on credit card fraud detection in the real world. The phenomenal growth in the number of credit card transactions has recently led to a considerable rise in fraudulent activities. The purpose is to obtain goods without paying or to obtain unauthorized funds from an account. As the usage of credit cards is increasing more, the chances of credit card frauds are also increasing dramatically. The credit card system is most vulnerable to fraud. These credit card frauds cost financial companies and consumers a very huge amount of money annually, fraudsters always try to find new methods and tricks to commit these illegal and outlaw actions. Online transaction fraud detection is the most challenging issue for banks and financial companies. So it is essential for banks and financial companies to have efficient fraud detection systems to reduce their losses due to these credit card fraud transactions. Various approaches have been found by many researchers to date to detect these frauds and to reduce them. Comparison of Local Outlier Factor and Isolation Factor algorithms using python and their detailed experimental results are proposed in this paper. After the analysis of the dataset we got the accuracy of 99% by random forest and 76% by Isolation Forest.