Reducing Food Waste in Campus Dining: A Data-Driven Approach to Demand Prediction and Sustainability

Tracking density in universities is essential for planning services like food, transportation, and social activities on campus. However, food waste remains a critical challenge in campus dining operations, leading to significant environmental and economic consequences. Addressing this issue is crucial not only for minimizing environmental impact but also for achieving sustainable operational efficiency. Campus food services significantly influence students' university choices; thus, forecasting meal consumption and preferences enables effective planning. This study tackles food waste by analyzing daily campus data with machine learning, revealing strategic insights related to food variety and sustainability. The algorithms Linear Regression, Extra Tree Regressor, Lasso, Decision Tree Regressor, XGBoost Regressor, and Gradient Boosting Regressor were used to predict food preferences and daily meal counts. Among these, the Lasso algorithm demonstrated the highest accuracy with an R2 metric value of 0.999, while the XGBRegressor also performed well with an R2 metric value of 0.882. The results underline that factors such as meal variety, counts, revenue, campus mobility, and temperature effectively influence food preferences. By balancing production with demand, this model significantly reduced food waste to 28%. This achievement highlights the potential for machine learning models to enhance sustainable dining services and operational efficiency on university campuses.