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Authors
Affiliations
1 Department of Computer Engineering, Marwadi Education Foundation, Rajkot, IN
2 Marwadi Education Foundation, Rajkot, IN
3 C.E. Department, R.K. University, Rajkot, IN
Source
Data Mining and Knowledge Engineering, Vol 7, No 6 (2015), Pagination: 209-213
Abstract
Modern years are taking great interest in knowing failure of an organization and also about the factors responsible for the failure of an institutes/organizations. Thus, organizations should take appropriate steps to improve the services given by them and also satisfies their employees. In educational organizations, students and teachers are most valuable assets. Results of students affects much more to this organizations. Hence, organizations are aiming to find risks factors behind the failure of students. So, in order to identify the risk factors various data mining techniques are applied and required result is formed. In this work, Experiments attempts to improve accuracy for predicting which student might fail or take drop out, using all available attributes, selecting best attributes using attribute selection methods available in WEKA and then sampling the data for experimentation and finally regression method is applied on whole data set. The outcomes are compared and modeled with the best result is shown.
Keywords
Educational Data Mining, Attribute Selection Methods Preprocessing, Stratified Sampling, Logistic Regression, Prediction.
