A PCA–Machine Learning Framework for Understanding Household Food Waste: Evidence from Young Urban Consumers in AlbaniaDietary Patterns, Waste Behaviors, and Demographic Drivers of Household Food Waste: Evidence from a Transitioning Food System

Elena Kokthi^*Fatmir Guri

• Universiteti Bujqesor i Tiranes, Tirana, Albania

This study explores the predictors of household food waste among young consumers (N = 414) in Albania, a country undergoing a transition from traditional to urbanised food systems. Using Principal Component Analysis (PCA), we identified eight dietary patterns, three waste patterns, three categories of reasons for food waste, and one dimension of dietary association. These components were analysed alongside demographic characteristics through Random Forest Regression (RFR) and Artificial Neural Networks (ANN) to evaluate their predictive capacity. The results show that food waste is systematically linked to dietary regimes: perishable fresh foods are wasted due to storage and planning deficits, while protein-and convenience-based diets drive waste through over-purchasing and portioning errors. Forest Regression (RFR) models consistently outperformed the Artificial Neural Networks (ANN) in predictive accuracy, with higher R² values (0.47–0.62 vs. 0.15–0.37) and lower error rates showingArtificial Neural Networks (ANNs) in predictive accuracy, with higher R² values (0.47–0.62 vs. 0.15–0.37) and lower error rates, demonstrating the strength of combining PCA with ML techniques. The findings highlight the behavioural pathways behind waste and provide a novel approach to modelling sustainability challenges in transitioning food systems.