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

机译:基于机器学习预测分析社会关注与巴基斯坦学生留学趋势的相关性

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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:我们存在于一个数字世界中,甚至小信息的共享和讨论来自世界各地的社交媒体。社交媒体对个人学习产生了很大影响,并在一个人的决策中发挥作用。在本文中,我们使用了机器学习和模式分类算法,看看社交媒体在线讨论论坛和人们的浏览行为之间是否存在任何相关性。我们已经分析并提出了一种新的铸造(预测)模型,了解巴基斯坦学生的社会关注与留学趋势的相关性。通过三种不同参数进行了分析,包括谷歌趋势工具与在线讨论论坛之间的趋势,季节性和相关的学生在不同国家在国外留学的一般信息,以利用不同的奖学金。相关性分析标识这两个变量之间的关系程度或依赖性。拟议的工作在数据挖掘和机器学习领域提供新颖的观点,以调查和预测巴基斯坦学生的课程趋于浏览行为与浏览行为。

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