<![CDATA[The rapid growth of digital advertising has underscored the need for data-driven strategies to optimize campaign performance. This study applies machine learning techniques to analyze online advertising data, aiming to identify key performance drivers and provide actionable insights for optimizing marketing strategies. The dataset includes metrics such as clicks, displays, costs, and revenue, which were preprocessed, analyzed, and modeled using ensemble methods, including Random Forest and Gradient Boosting. These ensemble methods were chosen for their ability to handle high-dimensional data, mitigate overfitting, and capture complex, nonlinear relationships between variables. Random Forest, with its bagging approach, enhances generalization by reducing variance, while Gradient Boosting incrementally corrects errors by focusing on hard-to-predict instances, improving overall predictive performance. Descriptive analysis revealed significant variability in campaign outcomes, with cost and user engagement emerging as primary predictors of revenue. Machine learning models demonstrated strong predictive accuracy, with Random Forest achieving 92% accuracy and an F1-score of 89%. Visualizations such as feature importance charts, correlation heatmaps, and learning curves validated the robustness of the models and highlighted key insights, including inefficiencies in cost allocation and the limited impact of certain categorical features like placement. The study emphasizes the potential of machine learning to optimize digital marketing strategies by identifying critical factors that influence campaign success. The findings provide a scalable framework for resource allocation, audience targeting, and strategic decision-making in online advertising. Future research could further enhance predictions by incorporating additional features, such as audience demographics and temporal trends, to provide deeper insights into campaign dynamics.]]>
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