Data mining approach for predicting student and institution's placement percentage

Abstract

Placement of students is one of the very important activities in educational institutions. Admission and reputation of institutions mainly depends on placements. Hence all institutions strive to strengthen placement department. In this study, the objective is to analyze previous year's student's historical data and predict placement chance of the current students and the percentage placement chance of the institution. A model is proposed along with an algorithm to predict the placement chance of students. Data pertaining to the study were collected form the same institution for which the placement chance prediction and percentage placement need to be found from 2006 to 2015. Data collected is divided into historic data form 2016to 1014 and test data i.e, 2014; 2016 data is considered as current data. Suitable data pre-processing methods are applied. Students having better chance of placement are characterized as good if not bad. This proposed model is compared with other classification algorithms such as Naïve bayes, Decision tree, and Neural network with respect to accuracy, precision and recall. From the results obtained it is found that the proposed algorithm predicts better in comparison with other algorithms.