Early Detection of Prone to Failure Student Using Machine Learning Techniques

被引：0
|
作者：
Kadam, Prabha Siddhesh [1 ]
Vaze, Vinod Moreshwar
机构：
[1] Shri JJT Univ, Comp Sci, Churela, Rajasthan, India
来源：
BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS | 2021年 / 14卷 / 05期
关键词：
MACHINE LEARNING; EARLY DETECTIONON; NAIVE BAYES CLASSIFIERS; LOGISTIC REGRESSION; SUPERVISED LEARNING;
D O I：
10.21786/bbrc/14.5/7
中图分类号：
Q81 [生物工程学（生物技术）]; Q93 [微生物学];
学科分类号：
071005 ; 0836 ; 090102 ; 100705 ;
摘要：
Machine learning techniques works on experience uses historical data and process them. The algorithms help to reveal facts and shows the path to move towards success. This study, uses for early detection of prone to failure using machine learning techniques. Supervised approach of machine learning used to analysis data in python colab environment. The Sample size 300 records used to evaluate data. Outcomes shows 82% accuracy with Naive Bayes classifiers. The study classifies records among three classes good, average and poor students.
引用
收藏
页码：36 / 39
页数：4
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