• Ngamrang Arangham, Mokchen Suayang
• Biswajit Das
• Sonali Mondal

Everyone is shifting to online purchases in this modern era, and credit cards have become crucial to this since they let people make payments without carrying cash. Although this method of payment is extremely helpful, there are some disadvantages as well. The total amount of credit card fraud has risen simultaneously with the increasing number of consumers with credit cards. Credit card details are being illegally collected by the fraudster, which can be used for unauthorized transactions. Some algorithms based on machine learning can be used to predict and stop fraudulent activities to tackle this problem. This paper compares a few well-known supervised learning algorithms to identify genuine and fraudulent transactions. The machine learning algorithms we use in this paper are logistic regression, linear regression, linear regression, and random forest.

Credit card, Random Forest, Logistic Regression, Machine Learning, and Fraud Prediction

"Supervised Machine Learning Techniques for Predicting Credit Card Fraud ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.p698-p702, April-2024, Available :http://www.jetir.org/papers/JETIR2404G90.pdf

"Supervised Machine Learning Techniques for Predicting Credit Card Fraud ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppp698-p702, April-2024, Available at : http://www.jetir.org/papers/JETIR2404G90.pdf