Happiness underlines the intuitive constructs of a specified population based on positive psychological outcomes. It is the cornerstone of the cognitive skills and exploring university student's happiness has been the essence of the researchers lately. In this study, we have analyzed the university student's happiness and its facets using statistical distribution charts; designing research questions. Furthermore, regression analysis, machine learning, and clustering algorithms were applied on the world happiness dataset and university student's dataset for training and testing respectively. Philosophy was the happiest department while Sociology the saddest; average happiness score being 2.8 and 2.44 respectively. Pearson coefficient of correlation was 0.74 for Health. Predicted happiness score was 5.2 and the goodness of model fit was 51%. train and test error being 0.52, 0.47 respectively. On a Confidence Interval(CI) of 5% p-value was least for Campus Environment(CE) and University Reputation(UR) and maximum for Extra-curricular Activities(ECA) and Work Balance(WB) (i.e. 0.184 and 0.228 respectively). RF with Clustering got the highest accuracy(89%) and F score(0.98) and the least error(17.91%), hence turned out to be best for our study

翻译：幸福基于积极心理结果，凸显特定人群的直观心理构建。它是认知技能的基石，探究大学幸福感近年来已成为研究者的核心课题。本研究利用统计分布图表分析大学幸福感及其维度，并设计研究问题。进一步，我们对世界幸福数据集和大学生数据集分别进行训练和测试，应用了回归分析、机器学习及聚类算法。哲学系幸福感最高，社会学系最低；平均幸福感得分分别为2.8和2.44。健康状况的皮尔逊相关系数为0.74。预测幸福感得分为5.2，模型拟合优度为51%，训练误差和测试误差分别为0.52和0.47。在5%置信区间下，校园环境和大学声誉的p值最小，课外活动与工作平衡的p值最大（分别为0.184和0.228）。结合聚类的随机森林算法获得了最高准确率（89%）、F值（0.98）及最低误差（17.91%），因此成为本研究的最佳模型。