Smart Agritech

19Leveraging Linear Regression Model to Address Food Insecurity in the United States: A Smart Agritech Approach

Anoushka Tomar*, Shivani Dubey, Vikas Singhal and Ajay Kumar Sahu

Department of Information Technology, Greater Noida Institute of Technology, Greater Noida, Uttar Pradesh, India

Abstract

Food insecurity is a widespread problem in the United States, impacting millions of households. It refers to the lack of access to sufficient and nutritious food, resulting in adverse health outcomes. The causes are multifaceted, including poverty, unemployment, low wages, and inadequate social safety net programs. Food insecurity leads to malnutrition, obesity, chronic diseases, and mental health issues, particularly affecting children and minorities. This chapter examines the causes, consequences, and potential solutions for food insecurity. It explores its prevalence among different demographic groups and highlights its links to poverty and unemployment. The consequences range from malnutrition to academic underachievement. Policy interventions such as SNAP, School Meal Programs, and WIC are analyzed. Datasets from various sources are used, and a comprehensive analysis is conducted, including a linear regression model. The findings can guide resource allocation. Correlations between food insecurity, poverty, health, and nutrition are established. The chapter concludes with recommendations for future research and policy actions to promote food security and equity for all Americans. ...