DOI:10.20894/IJDMTA.
Periodicity: Bi Annual.
Impact Factor:
SJIF:4.893 & GIF:0.787
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded
Educational data mining has emerged an on independent research area in recent years. Data related to field of education industry. This paper discusses about SSLC student taking decision for group selected suggestion. The biggest problem some of the student got above 85 in SSLC but at the same student merely lose the percentage in HSC. So here find out the factor for reducing mark. This problem may be come in non-interest group selection, or they felt difficult to read 12th syllabus. So this paper focused on student group select factor. The scope of this paper predicting the which group is match to the student, For their higher secondary education because The higher secondary education is important in student life because it is one of the factor that are going to decide the future of the student. Here collected the primary data through questionnaire among 12th and college student The questionnaire question about Which group in 11th std, why selected the this group , and mark in SSLC , interest subject in 10th std, and their ambition and ect., And these collection data apply to many data mining techniques in WEKA tool. Predicting the select good group to student is a great concern to the higher education managements. The scope of this paper is to investigate the accuracy of data mining techniques in such an environment and comparing the results obtained in WEKA. Classification techniques are used to classify each item in a set of data into one of predefined set of classes or groups. Classification methods like decision trees, Bayesian network etc can be applied on the educational data for predicting the student s group selection for higher secondary. There are many approaches that are used for data classification the decision tree (J48) method is used in this study. The Naive bayes, Multi Layer Perception, SMO, J48, REP Tree algorithms are applied on the student�s data to predict group select design. The study revealed that the Multi Layer Perception is more accurate than the other algorithms
