基于朴素贝叶斯算法的本科生学业成绩分析
Using Naive Bayes Algorithm to Students' bachelor Academic Performances Analysis
摘要：
学术数据采掘是一个新兴的领域，它包括通过不同的元素，如前学期的成绩，出勤率，作业，讨论，实验室工作来检查学生的详细信息的过程，用于提高本科生的学习成绩，并克服困难的低级别的本科生。它是从计算机系本科生数据中提取有用的知识。随后对数据进行预处理，应用数据采掘技术进行分类发现和聚类。本文提出了一种基于朴素贝叶斯算法的学术数据采掘分类方法。它支持学生和讲师评估学习成绩。这是一种警示性的方法，可以帮助学生提高学习成绩。
英文摘要：
Academic Data Mining was one of emerging field which comprise procedure of examined students details by different elements such as earlier semester marks, attendance, assignment, discussion, lab work were of used to improved bachelor academic performance of students, and overcome difficulties of low ranks of bachelor students. It was extracted useful knowledge from bachelor academic students data collected from department of Computing. Subsequently preprocessing data, which was applied data mining techniques to discover classification and clustering. In this study, classification method was described which was based on naive byes algorithm and used for Academic data mining. It was supportive to students along with to lecturers for evaluation of academic performance. It was cautionary method for students to progress their performance of study.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
