A Random Forest Based Analysis of Post-Covid Consumer Behavior in Organic Food Market

The COVID-19 pandemic changed the mind-set of a consumer, especially in the area of food habits, and eventually led the interest to grow within the area of natural food. The purpose of this study is to identify factors that significantly affect the buying behavior of organic food consumers in urban localities. A survey instrument was employed to gather the responses from the consumers. This study employed binary logistic regression technique and random forest classifier is used to identify the attributes that impacts consumer behavior. Random forest classifier outperformed the traditional logistic regression technique with 85% accuracy. The major findings of this study reveals several attributes that determine the consumer purchasing behavior such as quality concern and safety, availability, self-identity, taste, age, health consciousness, and environmental concerns. This research suggests academicians, policymakers, retailers, wholesalers by giving information on the attributes that influence the consumer buying behavior of chemical-free food consumers post-covid era and the strategies that should be adopted to overcome the challenges involved in the buying process.

• Publication year

  2025

• Venue

  International Conference on Computing for Sustainable Global Development

• Publication date

  2025-04-02

• Fields of study

  Not labeled

• Identifiers
  DOI 10.23919/INDIACom66777.2025.11115462
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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