Predicting Website user Engagement Using Machine Learning: A Behavioral and Design Feature Analysis
User engagement behavior is essential in predicting sales and profit for an organization. This study presents a machine learning-based framework for predicting website user engagement levels using behavioral and design-related features extracted from the Online Shoppers Purchasing Intention Dataset obtained from the UCI Machine Learning Repository. The dataset comprises 12,330 instances and 18 features representing user session characteristics, page interaction metrics, and temporal attributes. Four supervised machine learning models: Logistic Regression, Random Forest, XGBoost, and a one-dimensional Convolutional Neural Network (CNN) were developed and evaluated. Experimental results derived from actual model runs demonstrate that XGBoost achieved the highest Receiver Operating Characteristic Area under the Curve (ROC-AUC) of 0.929, followed by Random Forest with 0.918, then CNN with 0.906, and lastly, Logistic Regression having the value 0.865. Feature importance analysis identified PageValues, ProductRelated_Duration, and ExitRates as the most influential predictors. The findings provide actionable insights for optimizing website design and content strategy to improve user retention and conversion rates.
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