Swindling Shonky Anatomization of Credit Card Transactions using Machine Learning
M. Shyamala Devi1, Nariboyena Vijaya Sai Ram2, Aravapalli Sai Vamshi3, Basyam Bharath4, Mallangi Surya Prakash Reddy5
1M. Shyamala Devi, Associate Professor, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India.
2Nariboyena Vijaya Sai Ram, III Year B.Tech Student, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India.
3Aravapalli Sai Vamshi, III Year B.Tech Student, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India.
4Basyam Bharath, III Year B.Tech Student, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India.
5Mallangi Surya Prakash Reddy, III Year B.Tech Student, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 1477-1483 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7621118419/2019©BEIESP | DOI: 10.35940/ijrte.D7621.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the fast moving technological advancement, the internet usage has been increased rapidly in all the fields. The money transactions for all the applications like online shopping, banking transactions, bill settlement in any industries, online ticket booking for travel and hotels, Fees payment for educational organization, Payment for treatment to hospitals, Payment for super market and variety of applications are using online credit card transactions. This leads to the fraud usage of other accounts and transaction that result in the loss of service and profit to the institution. With this background, this paper focuses on predicting the fraudulent credit card transaction. The Credit Card Transaction dataset from KAGGLE machine learning Repository is used for prediction analysis. The analysis of fraudulent credit card transaction is achieved in four ways. Firstly, the relationship between the variables of the dataset is identified and represented by the graphical notations. Secondly, the feature importance of the dataset is identified using Random Forest, Ada boost, Logistic Regression, Decision Tree, Extra Tree, Gradient Boosting and Naive Bayes classifiers. Thirdly, the extracted feature importance if the credit card transaction dataset is fitted to Random Forest classifier, Ada boost classifier, Logistic Regression classifier, Decision Tree classifier, Extra Tree classifier, Gradient Boosting classifier and Naive Bayes classifier. Fourth, the Performance Analysis is done by analyzing the performance metrics like Accuracy, FScore, AUC Score, Precision and Recall. The implementation is done by python in Anaconda Spyder Navigator Integrated Development Environment. Experimental Results shows that the Decision Tree classifier have achieved the effective prediction with the precision of 1.0, recall of 1.0, FScore of 1.0 , AUC Score of 89.09 and Accuracy of 99.92%.
Keywords: Machine Learning, Recall, FScore, Accuracy and AUC Score.
Scope of the Article: Machine Learning.