Makine öğrenmesini kullanarak çevrimiçi reklamlara kullanıcı tıklamalarını tahmin etmek

This thesis explores the detection of user behavior within online advertising to better understand user preferences and refine ad strategies. As online ads are instrumental in reaching broad audiences, targeted approaches are vital for enhancing campaign effectiveness. The research employs data analysis, preprocessing, and machine learning (ML) techniques on a dataset detailing user behaviors like browsing history, ad clicks, and demographics. ML methods, including random forest, GB, and LR, are utilized alongside XAI tools like LIME and SHAP to highlight feature importance. The study aims to discern user behavior patterns to improve ad targeting. Models are further optimized using parameter tuning, and ensemble strategies, such as soft and hard voting, are used to aggregate individual predictions, boosting detection accuracy. Performance metrics, ranging from accuracy to specificity, are used to assess model efficacy. Results show that ensemble methods, particularly soft voting, outperform other techniques in accuracy.