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In today’s rapidly evolving world, recognizing student food choices is crucial. This study explores food choices and how they align with areas containing restaurants and grocery stores. Clustering techniques including K-Means, Hierarchical Clustering, and DBSCAN were employed with the silhouette score used to validate and determine the most effective method for analysis. Based on food choices data sourced from Kaggle and location data from the Foursquare API, the research provides location recommendations for students. Suggestions guide students to areas that align with their food choices aiming to enhance their overall experience.

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Clustering-Driven Insights for Recommending Ideal Student Locations

  • 会议论文

  • 发表日期：

    2026年

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  • S. D. Aswathkrishna,
  • M. Sujithra

摘要

In today’s rapidly evolving world, recognizing student food choices is crucial. This study explores food choices and how they align with areas containing restaurants and grocery stores. Clustering techniques including K-Means, Hierarchical Clustering, and DBSCAN were employed with the silhouette score used to validate and determine the most effective method for analysis. Based on food choices data sourced from Kaggle and location data from the Foursquare API, the research provides location recommendations for students. Suggestions guide students to areas that align with their food choices aiming to enhance their overall experience.