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The nutritional data exploration aims to provide users with a tool to visualize the impact of their dietary choices on various components. Simultaneously, the simulation model seeks to optimize nutritional intake in a hostel mess environment by utilizing real-world data on student preferences, gender, and physical activity levels. This study intertwines two facets of nutritional analysis—a user-centric approach using Python libraries and a simulation model for dietary nutrition. The user-centric analysis employs Python libraries like Pandas, Seaborn, SciPy, and SymPy to allow users to input and visualize deviations from standard nutritional values. Simultaneously, an Excel-based simulation model, driven by real-world data collected from 144 students, delves into institutional nutrition status considering factors like gender, food preferences, and physical activity. By leveraging Python’s analytical capabilities and Excel’s simulation tools, this exploration bridges individual dietary awareness with broader institutional nutritional planning.

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A Data-Driven Approach to Holistic Campus Wellness at National Institute of Technology Calicut

  • 会议论文

  • 发表日期：

    2026年

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  • Sabita Kumari,
  • Pragati Awasthi,
  • Vinay V. Panicker,
  • T. G. Pradeepmon

摘要

The nutritional data exploration aims to provide users with a tool to visualize the impact of their dietary choices on various components. Simultaneously, the simulation model seeks to optimize nutritional intake in a hostel mess environment by utilizing real-world data on student preferences, gender, and physical activity levels. This study intertwines two facets of nutritional analysis—a user-centric approach using Python libraries and a simulation model for dietary nutrition. The user-centric analysis employs Python libraries like Pandas, Seaborn, SciPy, and SymPy to allow users to input and visualize deviations from standard nutritional values. Simultaneously, an Excel-based simulation model, driven by real-world data collected from 144 students, delves into institutional nutrition status considering factors like gender, food preferences, and physical activity. By leveraging Python’s analytical capabilities and Excel’s simulation tools, this exploration bridges individual dietary awareness with broader institutional nutritional planning.