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Machine learning based forecasting for analyzing correlation between social attention and study abroad trend for Pakistani students

机译:基于机器学习的预测分析巴基斯坦学生的社会关注度与出国留学趋势之间的相关性

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We exist in a digital world where even small information is shared and discussed on social media from every part of the world. Social media have great impact on individual learning and play a role in one's decision making. In this paper, we have employed machine learning and pattern classification algorithms to see whether there exists any correlation between social media online discussion forums and people's browsing behavior. We have analyzed and proposed a new casting (predicting) model to understand the correlation between social attention and study abroad trend among Pakistani students. Analysis has been carried out with the three different parameters that include Tendency, Seasonality, and Correlation between the Google Trend tool and online discussion forums for students about the general information of studying abroad in different countries for availing different Scholarships. Correlation analysis identifies the degree of relationship or dependency between these two variables. Proposed work offers novel point of view in the area of data mining and machine learning to investigate and predict the relationship between study-abroad tend and browsing behavior of Pakistani students.
机译:我们存在于数字世界中,在这个数字世界中,甚至来自世界各地的社交媒体都可以共享和讨论很小的信息。社交媒体对个人学习产生重大影响,并在个人决策中发挥作用。在本文中,我们采用了机器学习和模式分类算法,以查看社交媒体在线讨论论坛与人们的浏览行为之间是否存在任何关联。我们已经分析并提出了一种新的投射(预测)模型,以了解巴基斯坦学生的社会关注度与出国留学趋势之间的相关性。已使用三个不同的参数进行了分析,包括趋势,季节性和Google趋势工具与在线讨论论坛之间的相关性,该论坛为学生提供有关在不同国家/地区出国留学以获取不同奖学金的一般信息。相关分析确定这两个变量之间的关系或依赖性程度。拟议的工作在数据挖掘和机器学习领域提供了新颖的观点,以调查和预测巴基斯坦留学生的出国学习倾向与浏览行为之间的关系。

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