• Janvi Ahuja
• Abhishek Jadoun
• Sadique Amin
• Prof.(Dr). Parul Gandhi

Credit card transactions Fraud is a major issue in financial sector, which is causing financial harm to both individuals & businesses .To mitigate these risks, machine learning models have been employed to detect fraudulent transactions. This study explores the performance of various classification models when using two different feature scaling techniques, Standard Scaler and Min Max Scaler. The goal is to compare the effectiveness of these scaling methods in enhancing the accuracy and precision of credit card fraud detection .In this research, we examine five commonly used classification models: Random Forest, K Neighbors Classifier, Logistic Regression, Decision Tree Classifier, and Gaussian NB. We assess their performance under two distinct scaling techniques: Standard Scaler and Min Max Scaler. The analysis is carried out on a dataset of credit card transactions, where the 'Class' variable categorizes transactions into two classes: non-fraudulent (Class 0) and fraudulent (Class 1).For each model and scaler combination, we measure performance using key evaluation metrics, including accuracy, precision, recall, F1 score, and ROC AUC score. Accuracy reflects the model's overall ability to correctly classify transactions, while precision indicates its accuracy in identifying fraudulent cases. Recall measures the model's capability to capture all actual frauds, while the F1 score provides a balanced assessment of precision and recall. The ROC AUC score evaluates the model's ability to distinguish between classes .The findings of this research reveal significant variations in the performance of classification models based on the choice of scaler. Notably, the Random Forest model demonstrates remarkable performance with both Standard Scaler and Min Max Scaler, achieving near-perfect accuracy, precision, and recall scores under Standard Scaler. This suggests that Random Forest is a robust choice for credit card fraud detection. K Neighbors Classifier exhibits a substantial improvement in performance when using Standard Scaler, achieving high accuracy, precision, and recall scores. Logistic Regression, too, benefits from Standard Scaler, resulting in enhanced accuracy and precision, indicating its suitability for fraud detection tasks. Decision Tree Classifier showcases a significant performance boost with Standard Scaler, exhibiting strong accuracy and precision, making it another favorable choice for this application .Gaussian NB, while less accurate than other models, still demonstrates reasonable performance, particularly with Standard Scaler. Overall, the choice of scaler plays a pivotal role in the success of credit card fraud detection models. Standard Scaler generally leads to superior model performance, particularly for Random Forest, K Neighbors Classifier, Logistic Regression, and Decision Tree Classifier. However, model selection remains crucial, as certain algorithms have limitations in capturing specific patterns and classifying cases .This study offers practical insights for financial institutions and organizations seeking to enhance their credit card fraud detection systems. It emphasizes the importance of careful model selection and the implementation of appropriate feature scaling techniques. The findings contribute to the ongoing efforts to combat credit card fraud effectively, reducing financial losses and safeguarding the interests of cardholders and businesses.

Credit card fraud detection, machine learning, classification ,Standard Scaler, Min-Max Scaler, model performance, feature scaling.

"Comparative Analysis of Credit Card Fraud Detection Using Standard Scaler and Min Max Scaler", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.e449-e456, February-2024, Available :http://www.jetir.org/papers/JETIR2402466.pdf

"Comparative Analysis of Credit Card Fraud Detection Using Standard Scaler and Min Max Scaler", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppe449-e456, February-2024, Available at : http://www.jetir.org/papers/JETIR2402466.pdf