Investigation the Effect of Covıd-19 Pandemic in The Sales for Online Education Using Machine Learning Methods

Sukran Seker, Merve Türkmen Ergün
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引用次数: 2

Abstract

Due to the pandemic which is the cause of the COVID-19 virus that emerged in 2019, many educational institutions had to follow online (remote) education. The situation in which the content of the pandemic occurred was also the reason for the education preferences of the users. The aim of this study is to analyze the effect of the pandemic, which includes the number of registered users of one of the online education platforms operating in Turkey thanks to machine learning, on distance education sales, and to create strategies by making sales forecasts for the future. Seven independent and one dependent variable were used to make sales forecasts using the data of the education structure. For accurate modelling, machine learning methods were first applied for decision analytics in a univariate manner and then multivariate applied and the applied methods were tested for error. By testing the success of the prediction models created with machine learning used in the study; 91.43% for support vector machine (SVM), 92.02% for multi-layer perceptron (MLP), and 96% for Long / Short Term Memory (LSTM). K-Folds cross validation method was also used for the success return of the established model.
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利用机器学习方法调查Covıd-19大流行对在线教育销售的影响
由于2019年新型冠状病毒感染症(COVID-19)的大流行,许多教育机构不得不采取在线(远程)教育。发生大流行病内容的情况也是用户对教育的偏好的原因。本研究的目的是分析疫情对远程教育销售的影响,包括通过机器学习在土耳其运营的一个在线教育平台的注册用户数量,并通过对未来的销售预测来制定策略。七独立和一个因变量被用来做销售预测使用数据的教育结构。为了准确建模,机器学习方法首先以单变量的方式应用于决策分析,然后以多变量的方式应用,并对应用的方法进行误差测试。通过测试研究中使用的机器学习创建的预测模型的成功;支持向量机(SVM)为91.43%,多层感知机(MLP)为92.02%,长短期记忆(LSTM)为96%。采用k - fold交叉验证方法对所建立的模型成功返回。
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