V Ramakrishna Sajja, Department of CSE, VFSTR deemed to be University, India
P Jhansi Lakshmi, Department of CSE, VFSTR deemed to be University, India
DS Bhupal Naik, Department of CSE, VFSTR deemed to be University, India
Hemantha Kumar Kalluri, Department of CSE, VFSTR deemed to be University, India
The enormous issue of drop out students or submissive is regarding scholarly accomplishment. The academic organization wants to catch up the prompting framework while counselors be supposed to direct the arranging educational programs to their advisors. The data mining methods are very much helpful to provide the qualitative education in the academic institutions and to analyze the student performance quickly. Different classification models which can be applied in academic data mining are focused in this paper. The student problem's can be identified by applying different classification models. The goal of this paper is to enhance the performance of student and at that point anticipating the appropriated scholarly accomplishment in each major. To look at the investigation, we utilized 1200 student’s data. Two measures like accuracy and error rate are assessed the framework. This method produces the 95.5% accuracy and 4.5% error rate.
Pre processing, Classification, Decision Tree, Training, Testing, Rule Extraction, Student Performance
[1] M Al-Razgan, A. S. Al-Khalifa, and H. S. Al-Khalifa, “Educational data mining: A systematic review of the published literature 2006- 2013”, In Proceedings the 1st International Conference on Advanced Data and Information Engineering, 2013, pp.711–719.
[2] Markus Hofmann, Ralf Klinkenberg,“RapidMiner: Data Mining Use Cases and Business Analytics Applications (Chapman and Hall/CRC Data Mining and Knowledge Discovery Series)”, CRC Press, October 25, 2013.
[3] J. R. Quinlan. “Induction of Decision Trees”. Mach. Learn. 1, 1 (Mar. 1986), 81–106.
[4] Kaur, Hardeep. “A Literature Review from 2011 TO 2014 on Student’s Academic Performance Prediction and Analysis using Decision Tree Algorithm.” Journal of Global Research in Computer Science 9.5 (2018): 10-15.
[5] Rao, NV Krishna, et al. “A Review on Data Mining Approach used in Education Data Mining using Decision Tree Algorithm.” (2018): 1735- 1738.
[6] Ahmed, Abeer Badr El Din, and Ibrahim Sayed Elaraby. “Data Mining: A prediction for Student’s Performance Using Classification Method.” World Journal of Computer Application and Technology 2.2 (2014): 43-47.
[7] J. Han, M. Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann, 2000.
[8] B. Kami´nski, M. Jakubczyk, P. Szufel. “A framework for sensitivity analysis of decision trees”. Central European Journal of Operations Research, 2017.
[9] Muhammad Arif and Amil Roohani Dar, "Survey on Fraud Detection Techniques Using Data Mining", International Journal of u - and e - Service, Science and Technology, SERSC Australia, ISSN: 2005-4246 (Print); 2207-9718 (Online), vol. 8, no.3, March (2015), pp. 163-170, http://dx.doi.org/10.14257/ijunnesst.2015.8.3.15.
[10] “Data Mining Curriculum”, ACM SIGKDD. 2006-04-30. Retrieved 2014-01-27.
[11] DS. Bhupal Naik, S. Deva Kumar, S. V. Ramakrishna, “Parallel Processing Of Enhanced K-Means Using OpenMP”, 2013 IEEE International Conference on Computational Intelligence and Computing Research, 2013, pp.1-4.
[12] Kaur, Parneet, Manpreet Singh, and Gurpreet Singh Josan. “Classification and prediction based data mining algorithms to predict slow learners in education sector.” Procedia Computer Science 57 (2015): 500-508.
[13] Venkatramaphanikumar S., et al., “A NOVEL PREDICTION MODEL FOR ACADEMIC EMOTIONAL PROGRESSION OF GRADUATES”, ARPN Journal of Engineering and Applied Sciences, VOL. 10, NO. 6, APRIL 2015.
[14] Liu Jin-sheng, Guoxi Sun, Qinghua Zhang and He jun, "Similarity Distance Noise Reduction of Entropy Based on Lifting KNN Classification Performance", International Journal of Security and Its Applications, SERSC Australia, ISSN: 1738-9976 (Print); 2207-9629 (Online), vol.9, no.2, February (2015), pp. 149-158, http://dx.doi.org/10.14257/ijsia.2015.9.2.14.
[15] Nasrin Kalanat and Mohammad Reza Kangavari, "Data Mining Methods for Rule Designing and Rule Triggering in Active Database Systems", International Journal of Database Theory and Application, SERSC Australia, ISSN: 2005-4270 (Print); 2207-9688 (Online), vol.8, no.1, February (2015), pp. 39-44, http://dx.doi.org/10.14257/ijdta.2015.8.1.05.
[16] Ji-Hoon Seo, Ho-Sun Lee and Jin-Tak Choi, "Classification Technique for Filtering Sentiment Vocabularies for the Enhancement of Accuracy of Opinion Mining", International Journal of u - and e - Service, Science and Technology, SERSC Australia, ISSN: 2005-4246 (Print); 2207-9718 (Online), vol. 8, no.10, October (2015), pp. 11-20, http://dx.doi.org/10.14257/ijunnesst.2015.8.10.02.
[17] Chao, Wang, and Wang Junzheng. “Cloud-service decision tree classification for education platform.” Cognitive Systems Research 52 (2018): 234-239.
[18] Bhanu Prakash Battula, KVSS Rama Krishna, Tai-hoon Kim, "An Efficient Approach for Knowledge Discovery in Decision Trees using Inter Quartile Range Transform", International Journal of Control and Automation, SERSC Australia, ISSN: 2005-4297 (Print); 2207-6387 (Online), vol. 8, no. 7, July (2015), pp. 325-334, http://dx.doi.org/10.14257/ijca.2015.8.7.32.
[19] Rokhmat Rokhmat, Yunus Indra Purnama, Eka Novita Sari and Tutut Herawan, "Applying Dependency of Attributes for Business Data Mining in Information Systems: A Theoretical Framework", International Journal of Control and Automation, SERSC Australia, ISSN: 2005-4297 (Print); 2207-6387 (Online), vol. 8, no. 4, April (2015), pp. 241-256, http://dx.doi.org/10.14257/ijca.2015.8.4.24.
[20] Shiliang Zhang and Tingcheng Chang, "A Hybrid Stock Selection Model Based on Forecasting, Classification and Feature Selection", International Journal of u - and e - Service, Science and Technology, SERSC Australia, ISSN: 2005-4246 (Print); 2207-9718 (Online), vol. 9, no.6, June (2016), pp. 153-168, http://dx.doi.org/10.14257/ijunnesst.2016.9.6.15.
APA:
Sajja, V. R., Lakshmi, P. J., Naik, D. S. B., & Kalluri, H. K. (2019). Prediction of Student Performance using Decision Tree Classifier. International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, 131, 1-12. doi: 10.33832/ijast.2019.131.01.
MLA:
Sajja, V Ramakrishna, et al. “Prediction of Student Performance using Decision Tree Classifier.” International Journal of Advanced Science and Technology, ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 131, 2019, pp. 1-12. IJAST, http://article.nadiapub.com/IJAST/Vol131/1.html.
IEEE:
[1] V. Ramakrishna Sajja, P. Jhansi Lakshmi, D. S. Bhupal Naik, and Hemantha Kumar Kalluri, “Prediction of Student Performance using Decision Tree Classifier.” International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 131, pp. 1-12, Oct 2019.
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