{"title":"使用机器学习来预测高中生的就业能力——一个案例研究","authors":"Aarushi Dubey, M. Mani","doi":"10.1109/DSAA.2019.00078","DOIUrl":null,"url":null,"abstract":"In this paper, we explore the use of supervised machine learning models to predict the employability of high school students with local businesses for part-time jobs. We further compare the performance of trained models used in this analysis to one another. Empirical results show that it is possible to predict the employability of high school students with local businesses with high-predictive accuracies. The trained predictive models perform better with larger dataset, with up to 93% accuracy.","PeriodicalId":416037,"journal":{"name":"2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Using Machine Learning to Predict High School Student Employability – A Case Study\",\"authors\":\"Aarushi Dubey, M. Mani\",\"doi\":\"10.1109/DSAA.2019.00078\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we explore the use of supervised machine learning models to predict the employability of high school students with local businesses for part-time jobs. We further compare the performance of trained models used in this analysis to one another. Empirical results show that it is possible to predict the employability of high school students with local businesses with high-predictive accuracies. The trained predictive models perform better with larger dataset, with up to 93% accuracy.\",\"PeriodicalId\":416037,\"journal\":{\"name\":\"2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)\",\"volume\":\"60 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DSAA.2019.00078\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSAA.2019.00078","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using Machine Learning to Predict High School Student Employability – A Case Study
In this paper, we explore the use of supervised machine learning models to predict the employability of high school students with local businesses for part-time jobs. We further compare the performance of trained models used in this analysis to one another. Empirical results show that it is possible to predict the employability of high school students with local businesses with high-predictive accuracies. The trained predictive models perform better with larger dataset, with up to 93% accuracy.