The purpose of data analytics is to uncover previously unknown patterns and make use of such patterns to help in making educated decisions across a wide range of contexts. Because of advances in modern technology and the fact that credit cards have become an easy target for fraudulent activity, the incidence of credit card fraud has considerably increased in recent years. Credit card fraud is a significant issue in the industry of financial services, and it results in annual losses of billions of dollars. The development of a fraud detection algorithm is a difficult endeavor due to the paucity of real-world transaction datasets that are available due to confidentiality concerns and the very unbalanced nature of the datasets that are publicly available. Use a dataset from the real world in conjunction with a variety of supervised machine learning algorithms to identify potentially fraudulent credit card transactions. In addition, make use of these techniques to create a super classifier through the use of ensemble learning methods. Determine which variables are the most significant and could perhaps lead to a higher level of accuracy in the identification of fraudulent credit card transactions. In addition, we evaluate and discuss the performance of a number of other supervised machine learning algorithms that are currently available in the literature in contrast to the super classifier that can be implemented.
