Data Mining and Knowledge Engineering

Priyanka D. Vaghasiya ¹, Sahista Machchhar ², Komal S. Sahedani ³

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

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.

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Priyanka D. Vaghasiya ¹, Sahista Machchhar ¹

Affiliations
1 Department of Computer Engineering, Marwadi Education Foundation, Rajkot, IN

Abstract

In recent year, huge amount of data related to education and particularly of students are stored in Database. To deal with these much data research communities showing greatest interest in data mining in Educational field. The goal of any educational organization is to increase the academic performance of students and ultimately progress of institute. To extract knowledge hidden within the dataset by means of analytical method is not easy. So the data mining techniques helps to transform knowledge into some human understable form. This paper provides literature available for educational data mining, what are the attributes which may affect the student's performance, how attribute selection methods are useful to select best attribute from available attributes, different data mining techniques used to know student's academic performance.

Keywords

Attribute Selection Methods, Data Mining, Educational Data Mining, Prediction.

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