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Student Performance Prediction System with Educational Data Mining
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 146 - Number 5 |
| Year of Publication: 2016 |
| Authors: Karishma B. Bhegade, Swati V. Shinde |
10.5120/ijca2016910704
|
Karishma B. Bhegade, Swati V. Shinde . Student Performance Prediction System with Educational Data Mining. International Journal of Computer Applications. 146, 5 ( Jul 2016), 32-35. DOI=10.5120/ijca2016910704
Abstract
In this paper we apply data mining tools to predict college failure and dropout. In Current year the researcher focuses on the new area of analysis like Educational data mining (EDM). Educational data mining techniques drawn from varied literatures which have data mining and machine learning. In this paper we are collecting the student’s information from Pimpri Chinchwad College of Engineering which comes under Pune University. We have preprocessed the information that we have collected for removal of unwanted information. Based on the classification rules student dropout and failure is being predicted. By using all available features, the experiments are conducted for improving the accuracy to predict which student has failed. In this paper C4.5 decision tree algorithm is proposed for prediction of students. C4.5 is the popular decision tree classifier in data mining. Accuracy of this classification algorithm is compared in order to check best performance. After tree building the ranking of the student is calculated on the basis of the student’s internal assessment. And then the frequent patterns are generated by using FP growth algorithm.
References
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