Prediction Model for Under-Graduating Student’s Salary Using Data Mining Techniques

Himanshi ¹ , Komal Kumar Bhatia²

¹ Department of Computer Engineering, YMCA University, Faridabad, Haryana, India.
² Department of Computer Engineering, YMCA University, Faridabad, Haryana, India.

Section:Research Paper, Product Type: Journal
Vol.6 , Issue.2 , pp.50-53, Apr-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrnsc/v6i2.5053

Online published on Apr 30, 2018

Copyright © Himanshi, Komal Kumar Bhatia . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
This paper reviews a salary prediction system using the profile of graduate students. A model is generated to present the predicted salary for individual students having similar attributes by using a data mining techniques. Data mining focuses on gathering knowledge from heterogeneous databases or data warehouses and extracting meaningful or interesting patterns from the collection. The data mining task is classified into two categories: descriptive tasks and predictive tasks. Descriptive tasks deal with the data in the database. Predictive tasks predict the class of the objects whose class label is unknown. Descriptive tasks include association, clustering, and summarization while predictive tasks include classification, prediction and time series analysis. The salary prediction model is designed using KNN classifier. In this work, we also made an experiment to compare data mining techniques including Decision trees, Naive Bayes, K-Nearest Neighbor on two databases with the different number of attributes.

Key-Words / Index Term :
Salary prediction system; data mining; Educational data mining; Classification technique

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