A COUPON RECOMMENDATION SYSTEM USING USER BEHAVIOR DATA

This project uses machine learning algorithms to develop a coupon recommendation system that predicts whether a user will accept a given coupon based on their demographics, driving context, and behavioral history. By applying exploratory data analysis (EDA), feature engineering, model evaluation, and hyperparameter tuning, this system enhances targeted marketing strategies and optimizes coupon distribution. Keywords: machine learning, classification, coupon acceptance prediction, recommendation system, user behavior analytics, Random Forest, logistic regression, hyperparameter tuning, scikit-learn, Python, EDA