Optimizing Ad Placements Using Epsilon-Greedy Algorithm Based on Device Type

Ad click prediction is vital for optimizing user engagement and enhancing digital advertising strategies. It involves predicting whether a user will click on an advertisement based on their interaction history and contextual factors. To address this, machine learning models such as Linear Regression, Logistic Regression, Random Forest, Decision Tree, and XGBoost are employed to build predictive systems. These models establish strong baselines for accuracy and performance. To further refine the predictions, the Epsilon-Greedy algorithm is applied, balancing exploration of alternative predictions and exploitation of existing model outputs. This approach ensures adaptability and optimization, contributing to more precise ad targeting and improved campaign efficiency.