Credit Card Fraud Detection using Logistic Regression with Imbalanced Dataset

Credit card fraud is a critical issue for financial institutions and their customers. In this study, we investigate the use of logistic regression, a machine learning algorithm, to detect fraudulent credit card transactions in an imbalanced dataset where only a small fraction of transactions are fraudulent. To address the issue of imbalanced data, we employ under sampling of the majority class and oversampling of the minority class. Our results show that the logistic regression model can achieve an accuracy of 94% in detecting fraudulent transactions. Additionally, we perform feature importance analysis to identify the most significant variables that contribute to fraud detection. Our findings suggest that variables such as transaction amount, country of origin, and time of day can be strong predictors of fraudulent transactions. our study highlights the potential of logistic regression for credit card fraud detection, even with an imbalanced dataset. The approach used in this study can contribute to the development of more sophisticated fraud detection models for financial institutions. Overall, our findings have practical implications for financial institutions seeking to improve their fraud detection capabilities, which can lead to a more secure and trustworthy financial system for all stakeholders involved.