In this project, we'll build a classification system to predict how customers will interact with upcoming marketing initiatives based on historical interaction data. We'll focus on handling imbalanced datasets by partitioning data with stratified sampling to maintain class proportions across training and testing sets. We'll train a logistic regression classifier to determine the likelihood of a positive campaign response and evaluate its effectiveness using a detailed confusion matrix. This approach allows us to identify high-probability responders, enabling the marketing team to optimize resource allocation by targeting only those most likely to convert.