Optimized Random Forest Classifier for Students Lifestyle Prediction Using Behavioral Data: A Machine Learning Approach

Abstract

Machine learning has increasingly been applied to behavioral analytics, yet its potential in lifestyle classification remains underexplored. This study utilizes a Random Forest classifier to predict lifestyle categories based on behavioral patterns from the Half a Million Lifestyle Dataset. A key challenge in lifestyle classification is balancing accuracy and generalization, which was addressed through parameter optimization to mitigate overfitting. To assess real-world applicability, 93 students provided behavioral inputs, which were processed through a Python-based program. The model successfully classified participants into Fitness Enthusiast (41), Health-Conscious (50), Eco-Friendly (1), and Social Media Influencer (1) categories, achieving an accuracy of 75.07%. These results confirm that machine learning can effectively predict lifestyle behaviors, with implications for personalized health interventions and behavioral analytics. This study underscores the significance of parameter tuning and feature selection, offering a scalable and data-driven approach to behavioral classification and wellness management.