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Hunger and food insecurity are present in each American county. Government and non-government organizations are working to address food insecurity in the United States. Food banks are nonprofit hunger relief organizations that collect food and monetary donations from donors and distribute food to local agencies which serve people in need. Contributions come from retail donors, communities, and food manufacturers. The uncertainty of donation amounts and frequency is a challenge for food banks in the fight against hunger. In this research, we analyze local food bank donation data and propose a predictive model to forecast the contribution of different donors. Our study shows the necessary behavioral attributes to classify donors and the best way to cluster donor data to improve the prediction model. We also compare the accuracy of prediction for different conventional forecasting techniques with the proposed Support Vector Regression (SVR) model.