Comparative Analysis of Logistic Regression and Decision Tree Algorithms for Credit Card Fraud Detection

Credit card fraud is a serious threat to financial institutions and consumers. The methods for detectionand prevention must be improved. This study compares the analysis of two leading machine learningalgorithms and logistic regression decision trees in the context of credit card fraud detection. Logisticregression is a statistical model that estimates the likelihood of fraud based on various behaviouralcharacteristics. while a decision tree classifies transactions as fraudulent or legitimate, creating the study will evaluatethese algorithms using a dataset of credit card transactions. Performance is assessed through metrics suchas precision, precision, recall, computational efficiency, etc. Logistic regression offers a probabilisticapproach. This may provide a more detailed view of transaction risk. While decision trees have Ability tomanage complex and non-linear relationships in data. Give the correct model The results indicate thatalthough logistic regression is good at classifying probabilities and can be very effective with appropriateparameters, Decision trees provide additional explanation and adapt to different fraud models.Comparative analysis reveals the strengths and limitations of each algorithm. It is an optimal technique for detecting fraud in different, scenarios, providing valuable insights forselection. The findings aim to increase understanding of machine learning applications in the financialsecurity field. and to guide future research and practical applications in fraud prevention strategies.