STUDENT PERFORMANCE PREDICTION USING CLASSIFICATION DATA MINING TECHNIQUES
Shiwani suryawanshi
, Vinaya patil , Mayur saner , Viplav patil , Bhushan sarode
dropout, prediction, classification, data mining, education.
Students opting engineering as their disciple is increasing rapidly. But due to various factors and inappropriate primary education in India dropout rates are high. Students are unable to excel in core engineering subjects which are complex and mathematical, hence mostly get drop / keep term (kt) in that subject. With the help of data mining techniques we can predict the performance of students in terms of grades and dropout for a subject can be predicted. In the proposed system, Naïve Bayes algorithm is used. Based on the rules obtained from the developed technique, the system can derive the key factors influencing student performance.
"STUDENT PERFORMANCE PREDICTION USING CLASSIFICATION DATA MINING TECHNIQUES", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.2, Issue 6, page no.163 - 167, June-2017, Available :https://ijsdr.org/papers/IJSDR1706021.pdf
Volume 2
Issue 6,
June-2017
Pages : 163 - 167
Paper Reg. ID: IJSDR_170496
Published Paper Id: IJSDR1706021
Downloads: 000347168
Research Area: Engineering
Country: jalgaon, maharashtra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
