Enhancing Computational Science Education Through Practical Applications: Leveraging Predictive Analytics in Box Meal Services

This paper presents a student project carried out in collaboration with a major industry partner, demonstrating the simultaneous novel application of predictive analytics, in particular machine learning (ML), in the domain of boxed meal services, and explores its implications for IT education. Drawing from a validated ML model trained on data collected from box meal companies, this study showcases how predictive analytics can accurately predict customer sociodemographic characteristics, thereby facilitating targeted marketing strategies and personalized service offerings. By elucidating the methodology and results of the ML model, this article demonstrates the practical utility of computational techniques in real-world electronic services. Moreover, it discusses the pedagogical implications of incorporating such case studies into computational science education, highlighting the opportunities for experiential learning, interdisciplinary collaboration, and industry relevance. Through this exploration, the article contributes to the discourse on innovative teaching methodologies in computational science, emphasizing the importance of bridging theory with practical applications to prepare students for diverse career pathways in the digital era.