Food Requirement Analysis Using Clustering Algorithms

Understanding the diverse food requirements within a population is critical for public health policy, personalized nutrition, and efficient food supply chain management. Traditional methods often rely on broad demographic averages, failing to capture the nuanced dietary patterns of smaller subgroups. This paper proposes a data-driven framework using unsupervised machine learning to analyze and segment food requirements. We leverage clustering algorithms, specifically K-Means and Agglomerative Hierarchical Clustering, to identify distinct dietary profiles from a dataset comprising demographic, anthropometric, and lifestyle features. The dataset is pre-processed, and features are scaled to ensure algorithmic efficacy. The optimal number of clusters is determined using the Elbow method and Silhouette Score analysis. The resulting clusters reveal distinct, interpretable groups such as “Active Young Adults,” “Sedentary Middle-Aged Individuals,” and “At-Risk Seniors,” each with unique nutritional needs. The findings demonstrate that clustering is a powerful tool for uncovering hidden patterns in food consumption, enabling targeted nutritional interventions and more effective resource allocation. This approach provides a scalable and granular alternative to conventional population analysis.