Comparative analysis of credit card fraud detection using logistic regression with random forest towards an increase in accuracy of prediction

MV Krishna, J Praveenchandar - … International Conference on …, 2022 - ieeexplore.ieee.org
MV Krishna, J Praveenchandar
2022 International Conference on Edge Computing and Applications …, 2022•ieeexplore.ieee.org
The study aims to identify the frauds committed using a payment card such as credit cards,
debit cards, and also an experiment is performed to find the best suitable algorithm among
Random forest and Logistic Regression. Materials and Methods: To stop the fraud
detections using Random forest (N= 10) and Logistic regression (N= 10) with supervised
learning that gives insights from the previous data. Results: The precision of the random
forest is 76.29% compared with Logistic regression with accuracy of 74.65% with statistical …
The study aims to identify the frauds committed using a payment card such as credit cards, debit cards, and also an experiment is performed to find the best suitable algorithm among Random forest and Logistic Regression.
Materials and Methods
To stop the fraud detections using Random forest (N=10) and Logistic regression (N=10) with supervised learning that gives insights from the previous data.
Results
The precision of the random forest is 76.29% compared with Logistic regression with accuracy of 74.65% with statistical significance value p=0.03 (p<0.05) using Independent sample t test.
Conclusion
This results proved that Random forest was significantly better for Fraud detection than Logistic regression within the study’s limits.
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