Predictive Data Modeling: Educational Data Classification and Comparative Analysis of Classifiers Using Python

Abstract

Due to an increase in the number of data sources and digital community, there is a huge amount of unstructured data at almost every synergy and in such outline, data mining becomes an important field of Machine Learning. Machine learning can be used for data mining following its two approaches i.e. Supervised learning and Unsupervised learning to find out meaningful information from huge accumulated unstructured data. To increase the quality of education and to find a solution to problems soaring from the complicated educational dataset and contentious environment among the academic institutions, educational data mining is receiving great attention. Educational data mining helps in facilitation and utilization of resources related to student performance, predicting placement results and finding new educational trends. In this paper, classification of student's data in terms of internal assessment given by faculty members and visualization of an educational dataset using Python following multiple Data based classification prediction models and comparative results of classifier models are performed. The classifier models using python which can transform learning are compared and the model having best accuracy measure is considered for predictive analytics and classification of the overall performance of class.