A Machine Learning Strategy for Reducing Childhood Obesity Using Millet

To gain a more comprehensive understanding of the complex correlation between millets and the management of pediatric obesity, this research employs machine learning techniques. We meticulously assess the impact of a nutritional intervention utilizing millet on the body mass index (BMI), dietary patterns, and physical activity levels among children. Employing feature engineering, data preprocessing, and model training, this study enhances its outcomes via regression and classification analyses. The findings should shed light on concealed patterns, ascertain crucial predictors, and expand our comprehension of the potential of millets in mitigating childhood obesity. This methodology presents a novel outlook on the complex issues surrounding childhood obesity and the immense potential of millets in fostering a healthy lifestyle through the integration of machine learning techniques and nutritional research.