PREDICTIVE MODELING OF CLICK-THROUGH RATES: A REGRESSION ANALYSIS APPROACH

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Volume 6 (2), December 2023, Pages 199-202

Suleyman Suleymanzade

Institute of Information Technology, Baku, Azerbaijan, This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

This research uses advanced regression techniques to develop a robust predictive model for Click-Through Rates (CTR) in online advertising. The study leverages a diverse dataset encompassing various advertising campaigns and user interactions to uncover patterns and relationships influencing click-through behavior. The goal is to provide advertisers with a tool for accurate CTR prediction, enabling them to optimize campaigns and allocate resources effectively.

Keywords:

Data Splitting, XGBoost, CTR-related, CTR Prediction.

DOI: https://doi.org/10.32010/26166127.2023.6.2.199.202

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