Data-driven strategies for nutritious diet design

Abstract

This project develops a data-driven framework to generate personalized, nutrient-rich dietary recommendations tailored to the diverse eating habits and common deficiencies found within the Indian population. We integrate a comprehensive dataset—encompassing user profiles, health metrics, dietary preferences, and culturally relevant ingredient nutrient tables—into a robust analytical pipeline. The workflow incorporates systematic data preprocessing, exploratory data analysis (EDA), and feature extraction through Principal Component Analysis (PCA) to isolate critical nutrient variables. We then apply clustering algorithms and nutrient classification heuristics to uncover distinct dietary pattern groups. These insights fuel a recommendation engine that suggests individualized, RDA-compliant meal plans and substitutions. To achieve structured nutrient profiles from real-world recipe data, we also introduce a standardized ingredient parsing pipeline that transforms raw textual input into normalized nutritional attributes. By combining computational modeling, nutrition science, and scalable data architectures, this approach addresses nutritional gaps and facilitates culturally resonant, health-optimized dietary designs.