Data Mining for Students’ Employability Prediction

Abstract

This study has been undertaken to predict the student employability.Assessing student employability provides a method of integrating student abilities and employer business requirements, which is becoming an increasingly important concern for academic institutions. Improving student evaluation techniques for employability can help students to have a better understanding of business organizations and find the right one for them. The data for the training classification models is gathered through a survey in which students are asked to fill out a questionnaire in which they may indicate their abilities and academic achievement. This information may be used to determine their competency in a variety of skill categories, including soft skills, problem-solving skills and technical abilities and so on.The goal of this research is to use data mining to predict student employability by considering different factors such as skills that the students have gained during their diploma level and time duration with respect to the knowledge they have captured when they expect the placement at the end of graduation. Further during this research most specific skills with relevant to each job category also was identified. In this research for the prediction of the student employability different data mining models such as such as KNN, Naive Bayer’s, and Decision Tree were evaluated and out of that best model also was identified for this institute's student’s employability prediction.So, in this research classification and association techniques were used and evaluated.