Data Mining: A prediction for performance improvement using classification

TL;DR

This paper develops a classification-based predictive data mining model to analyze educational data, aiming to distinguish high-performing students from slower learners in Indian higher education.

Contribution

It introduces an experimental methodology for preprocessing educational data and constructs a predictive model using Naive Bayes classification to improve student performance analysis.

Findings

Successfully generated a database of 300 student records

Developed a Naive Bayes classification model for student performance prediction

Demonstrated the effectiveness of data preprocessing in educational data mining

Abstract

Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students' performance. The performance in higher education in India is a turning point in the academics for all students. This academic performance is influenced by many factors, therefore it is essential to develop predictive data mining model for students' performance so as to identify the difference between high learners and slow learners student. In the present investigation, an experimental methodology was adopted to generate a database. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 300 student records, which were used for by Byes classification prediction model construction. Keywords- Data Mining, Educational Data Mining, Predictive Model, Classification.