This project analyzes the UCI Bank Marketing dataset using R to predict customer subscription behavior for term deposits. Logistic Regression and Naive Bayes models were applied and compared using performance metrics such as accuracy, sensitivity, specificity, and AUC. The analysis identifies key demographic and campaign factors influencing customer responses and offers actionable marketing insights.