Machine learning approaches for accurate energy content prediction in foods using nutritional data

This study marks a step toward more effectively translating nutritional information to inform public health policy as well as individual dietary choices. Motivated by the increase in diet-related health issues, this research aims to analyze a comprehensive nutritional dataset to uncover valuable insights. Using the USDA National Nutrient Database, the study employs data preprocessing to clean the data, exploratory data analysis to identify hidden patterns, and various machine learning models to predict nutritional values. The results demonstrate the usefulness of these models in explaining the composition data and highlight a range of trends and relationships within the observed amounts. The discussion emphasizes that these findings could be instrumental in guiding health professionals and policymakers toward healthier dietary guidelines. The significance of this research lies in its potential to advance public health through more sophisticated nutritional recommendations.