Identification of unhealthy eating patterns are a significant public health problem with recent economic and health consequences. Although many dietary preferences are formed early, the transition to independent living during the university years is a significant event because as people mature, they become more capable of making their own eating decisions. This study's objective was to use a questionnaire to assess eating patterns in relation to students' sociodemographic factors. One hundred students completed matched questionnaires describing pronounced snack intake, eating intentions, and a healthy diet. This study looks at how women and babies should eat, and it uses Google Docs to describe and analyze the facts about food intake. Through examining the content of documents pertaining to eating behavior, particularly with regard to fruits, milk, vegetables, and fast food, a series and evaluation of information has been provided. The findings of this investigation may be utilized to make recommendations regarding how to measure students' drinking habit in relation to their health using IBM SPSS Statistics 21.0's machine learning based k-means cluster evaluation.

Conscious, Questionnaire dataset, Behavior Identification, K-means clustering, SPSS.