Data-Driven Profit Maximization in Marketing Campaign: A Logistic Regression Approach.

This paper investigates the application for predicting consumer responses in marketing campaigns using a comprehensive dataset encompassing demographic, behavioural, and purchasing data. Through extensive feature engineering, followed by constructing and evaluating a logistic regression model. Key predictors influencing consumer response are identified, shedding light on their impact. The findings underscore the significance of income levels and specific spending behaviours, particularly on products like fish and wine, in influencing consumer perceptions and campaign responses. Insights from this research contribute to refining marketing strategies aimed at maximizing consumer engagement and satisfaction.