Analyzing Rural Distress Through MGNREGA Employment Demand And Agriculture Factures Trends Using Machine Learning

Rural distress remains a major socio-economic challenge in agrarian economies, particularly in India where livelihoods are highly dependent on agriculture and climatic conditions.Variations in rainfall, crop productivity, and seasonal employment often lead to income instability among rural households.The Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) serves as a crucial social safety net by providing guaranteed wage employment during periods of agricultural stress.This paper presents a machine learningbased analysis of rural distress by examining the relationship between MGNREGA employment demand and key agricultural indicators such as crop production and rainfall.Publicly available government datasets are used for analysis, and supervised learning models including Linear Regression, Decision Tree, Random Forest, and Support Vector Machine are applied.Experimental results show that adverse agricultural conditions are strongly associated with increased MGNREGA participation, highlighting the responsiveness of the scheme to rural economic stress.The study demonstrates the effectiveness of machine learning techniques in supporting data-driven assessment of rural distress and informed policy planning.