Predicting The Consumer Purchase Behavior Of Organic Food Using Decision Tree Algorithm

Abstract

Consumers have become more conscious of health and diet. Consequently, they are interested to take better care of their health by having healthier food. Thus, the consumer purchase behavior of organic food is an important study area for businesses and researchers nowadays. This study presents an approach to predicting consumer purchase behavior of organic food using the decision tree algorithm. Decision trees are a popular machine-learning technique that can provide insights into consumer decision factors. The present study focused on demographic characteristics, purchase behavior, product satisfaction, product knowledge from media, health consciousness, and food safety of university students in tier 2 cities in Karnataka. A decision tree model can be built and evaluated by collecting relevant data on consumer demographics, income levels, health consciousness, environmental awareness, and previous organic food purchase history. The insights into the decision tree structure provide valuable information to businesses seeking to recognize the features motivating organic food purchases and prepare their strategies.