{"title":"Machine Learning-Based Computational Optimization of Performance Prediction Model","authors":"Jyoti Upadhyay, Farhat Anjum, Chetna Sahu","doi":"10.46610/jodmm.2022.v07i03.001","DOIUrl":null,"url":null,"abstract":"Predicting student success in advance can help educational institutions enhance their teaching quality. This research offers insight into predicting student success not only based on academic information but also on their social structure and living area. The goal of this study is to predict students' grades using machine learning based models such as Decision Tree, Linear Regressor, and Random Forest Regressor and to select the best model among these three.","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"33 1","pages":""},"PeriodicalIF":0.4000,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Data Mining Modelling and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46610/jodmm.2022.v07i03.001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Predicting student success in advance can help educational institutions enhance their teaching quality. This research offers insight into predicting student success not only based on academic information but also on their social structure and living area. The goal of this study is to predict students' grades using machine learning based models such as Decision Tree, Linear Regressor, and Random Forest Regressor and to select the best model among these three.
期刊介绍：
Facilitating transformation from data to information to knowledge is paramount for organisations. Companies are flooded with data and conflicting information, but with limited real usable knowledge. However, rarely should a process be looked at from limited angles or in parts. Isolated islands of data mining, modelling and management (DMMM) should be connected. IJDMMM highlightes integration of DMMM, statistics/machine learning/databases, each element of data chain management, types of information, algorithms in software; from data pre-processing to post-processing; between theory and applications. Topics covered include: -Artificial intelligence- Biomedical science- Business analytics/intelligence, process modelling- Computer science, database management systems- Data management, mining, modelling, warehousing- Engineering- Environmental science, environment (ecoinformatics)- Information systems/technology, telecommunications/networking- Management science, operations research, mathematics/statistics- Social sciences- Business/economics, (computational) finance- Healthcare, medicine, pharmaceuticals- (Computational) chemistry, biology (bioinformatics)- Sustainable mobility systems, intelligent transportation systems- National security
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