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Happiness underlines the intuitive constructs of a specified population basedon positive psychological outcomes. It is the cornerstone of the cognitiveskills and exploring university student's happiness has been the essence of theresearchers lately. In this study, we have analyzed the university student'shappiness and its facets using statistical distribution charts; designingresearch questions. Furthermore, regression analysis, machine learning, andclustering algorithms were applied on the world happiness dataset anduniversity student's dataset for training and testing respectively. Philosophywas the happiest department while Sociology the saddest; average happinessscore being 2.8 and 2.44 respectively. Pearson coefficient of correlation was0.74 for Health. Predicted happiness score was 5.2 and the goodness of modelfit was 51%. train and test error being 0.52, 0.47 respectively. On aConfidence Interval(CI) of 5% p-value was least for Campus Environment(CE) andUniversity Reputation(UR) and maximum for Extra-curricular Activities(ECA) andWork Balance(WB) (i.e. 0.184 and 0.228 respectively). RF with Clustering gotthe highest accuracy(89%) and F score(0.98) and the least error(17.91%), henceturned out to be best for our study

Introspecting the Happiness amongst University Students using Machine Learning