Please use this identifier to cite or link to this item: http://dspace.azjhpc.org/xmlui/handle/123456789/268
Title: Predictive Modeling of Click-Through Rates: A Regression Analysis Approach
Authors: Suleymanzade, Suleyman
Keywords: Data Splitting;CTR-related;XGBoost;CTR Prediction
Issue Date: 1-Dec-2023
Publisher: Azerbaijan Journal of High Performance Computing
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.
URI: http://dspace.azjhpc.org/xmlui/handle/123456789/268
ISSN: 2616-6127 2617-4383
Journal Title: Azerbaijan Journal of High Performance Computing
Volume: 6
Issue: 2
First page number: 199
Last page number: 202
Number of pages: 4
Appears in Collections:Azerbaijan Journal of High Performance Computing

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