MACHINE LEARNING-BASED PREDICTIVE MODELING FOR USER ENGAGEMENT ANALYSIS

Abstract

This study proposes a machine learning-based predictive modeling framework for analyzing and forecasting user engagement in digital environments. With the increasing importance of engagement metrics for platform growth and user retention, accurately predicting engagement has become a critical challenge in data science and artificial intelligence. The research utilizes a structured dataset comprising interaction and content-related features, where user engagement is derived using a log-transformed combination of key interaction metrics. A comprehensive methodology involving data preprocessing, feature engineering, and model development is employed to ensure robust analysis. Multiple machine learning models, including Linear Regression, Random Forest, and Gradient Boosting, are implemented and evaluated using standard performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results demonstrate that ensemble models, particularly Gradient Boosting, outperform traditional approaches by effectively capturing non-linear relationships and complex feature interactions. Additionally, feature importance analysis identifies key predictors influencing engagement, providing actionable insights for optimizing digital platforms. The study highlights the significance of combining predictive accuracy with model interpretability to support practical applications. Overall, the proposed framework offers a scalable and efficient solution for user engagement analysis in modern data-driven systems.