Regression Analysis of Factors Affecting Child Undernutrition

Child undernutrition remains a pressing global concern, especially in developing regions. This research paper investigates the relationship between child undernutrition and critical factors, including women's education, maternal health, pregnancy-related challenges, maternal age at first childbirth, income indicators, and government healthcare expenditure. Uti- lizing a carefully curated dataset, this study explores these factors, focusing on the challenges faced by developing nations like India. Through exploratory data analysis and regression analyses, including Ordinary Least Squares (OLS), Random Forest Regression, and Gradient Boosting Regression, the paper uncovers significant insights. The Random Forest Regression model emerged as the best-performing model, achieving an impressive accuracy of 98.9%. These findings provide valuable in- sights for policymakers, healthcare professionals, and researchers addressing child undernutrition in developing regions.