Clickthrough Rate Prediction with Tree-based Machine Learning Models

Blessington Naveen Palaparthi; S. Akthar

doi:10.55524/ijirem.2025.12.1.4

Abstract

This paper introduces an intuitive approach to clickthrough rate (CTR) prediction, a learning problem that has been extensively studied over the past several years. As digital marketing continues to grow rapidly into a multi-billion-dollar industry, this study aims to find the most effective machine learning model to enhance the CTR of marketing emails by comparing various tree-based models. Key steps in this research include data collection, feature extraction, and CTR prediction through the evaluation of different models. The statistical results prove that the CatBoost model, with optimized feature selection, achieves near-perfect data fitting, indicating its efficiency.

Keywords

Click Through Rate (CTR), Contextual Advertisements, Email Marketing, Web advertisements, Machine Learning, Decision tree, Logistic Regression, CatBoost Regression, Random Forest Regressor, Extra Trees Regressor, LightGBM, Gradient Boosting

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Cites this article as

B. N. Palaparthi, S. Akthar, "Clickthrough Rate Prediction with Tree-based Machine Learning Models", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-12, Issue-1, Page No-28-33, 2025. Available from: https://doi.org/10.55524/ijirem.2025.12.1.4