CREDIT CARD FRAUD DETECTION USING LOGISTIC REGRESSION AND ADAPTIVE TRAINING STRATEGIES

The increasing reliance on digital transactions has led to a surge in credit card fraud, which poses significant financial risks to individuals and institutions. Traditional fraud-detection systems often struggle because of the highly imbalanced nature of transaction datasets, in which fraudulent cases represent only a small fraction of all transactions. This study addresses these challenges by developing a machine-learning-based fraud detection system using Logistic Regression and adaptive training strategies. To improve the detection accuracy, the Synthetic Minority Oversampling Technique (SMOTE) was applied to handle class imbalance by generating synthetic fraudulent samples. Additionally, feature scaling using StandardScaler ensures that all features are normalized for better model performance. The proposed system was trained and evaluated using various performance metrics, including the accuracy (96.49%), precision (98%), recall (95%), and F1-score (96%). The model achieved an impressive ROC-AUC score of 0.9935, demonstrating its ability to effectively distinguish between legitimate and fraudulent transactions. The results highlight the effectiveness of combining Logistic Regression with data preprocessing techniques to enhance fraud detection in imbalanced datasets.