Predictive Model for Partner Agencies Dependency on Food Banks

Abstract

In the quest for equitable resource distribution within food banks and their partner agencies, understanding the dependencies of these agencies on food banks emerges as a critical factor. This study investigates the intricate dynamics influencing agency dependency ratios, exploring the complex factors that shape the demand for food resources. Leveraging historical self-reported dependency ratio data, this preliminary study employs predictive modeling using Multiple Linear Regression to forecast agency dependencies on food banks. The primary objective is to discern the underlying factors that significantly impact agency dependency ratios. Employing Least Absolute Shrinkage and Selection Operator (LASSO) as a feature selection technique, the study identifies the key variables that capture the essence of the dataset. Identifying the variables that contribute the most to the model paves the way for robust predictive modeling. This study offers a comprehensive approach to understanding and predicting agency dependencies on food banks. The findings hold significant implications for non-profit hunger relief organizations, aiding in strategic decision- making for equitable resource distribution.

Keywords: Food banks, Partner agencies dependency, Prediction, Regression Model

DOI: 10.54941/ahfe1005573