Predicting Student’s Coming Target after Completion of Their Graduation by Mining Trained Database using Decision Tree
Sanjeev Gour1, Apoorva Joshi2

1Dr. Sanjeev Gour, Associate Professor in Department of Computer Science in Career College, Bhopal. India.
2Apoorva Joshi, Research Scholar in RNTU University Bhopal. India.

Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 2918-2922
| Volume-8 Issue-8, June 2019 | Retrieval Number: H7282068819/19©BEIESP

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Abstract: Data mining techniques have great capability to discover or find hidden knowledge from large data. As every educational institute produce large data about various academic and nonacademic activities of their students in every year. This is why many emerging data mining techniques are widely used in educational domain to extract useful knowledge from huge database and this knowledge are then used for various educational decisions. Educational institute can predict or understand the academic and social behaviors of their students by mining their past data. This study, similarly, emphasize on predicting student’s coming academic or career target after completion of their graduation by analyzing past database which is generated by filling by them at the time of admission. This prediction can be used to understand the future behavior of the student so that educational institute could make better decision policy. One of data mining technique called decision tree is applied on trained database of students which is collected from department of computer science of Career College Bhopal using Rapid Miner tool. Many interesting and useful rules are extracted which help to make better decision policy in an educational institute.
Keyword: Decision Tree, Educational Data Mining, Prediction Analysis, Rapid Miner.
Scope of the Article: Database Theory and Application.