Prediction Model for Under-Graduating Student’s Salary Using Data Mining Techniques
Keywords:
Salary prediction system, data mining, Educational data mining, Classification techniqueAbstract
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.
References
A. Parkavi1, K. Lakshmi “Predicting the Course Knowledge Level of Students using Data Mining Techniques “,IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), (2017).
A. M Shahiri, W. Husain and N.A Rashid, “A Review on Predicting Students’ Performance using Data mining Techniques,” in The Third Information System Information Council
R.A. Huebner, “A survey of educational data mining research,” Research in Higher Education Journal.
Improving Students’ Motivation to Study using Salary Prediction System Pornthep Khongchai, Pokpong Songmuang Department of Computer Science Faculty of Science and Technology
S. Anupama Kumar Vijayalakshmi M.N. “Inference of Naïve Baye’s Technique on Student Assessment Data”, R.V. College of Engineering, Bangalore, India
John Jerrim, “Do college students make better predictions of their future income than young adults in the labor force?”, Education Economics, 23:2, p 162-179, 2013.
Karlar Hamlen and William A. Hamlen,” Faculty Salary as a predictor of student outgoing salaries from MBA programs”, Journal of Education for Business
Bhaskar N. Patel, Satish G. Prajapati, and Dr. Kamaljit I. Lakhtaria, "Efficient Classification of Data Using Decision Tree”
Kayah, F. “Discretizing Continuous Features for Naive Bayes and C4. Classifiers”. University of Maryland publications: College Park, MD, USA.
S. Taruna, Mrinal Pandey, “An empirical analysis of classification techniques for predicting academic performance”, IEEE Advances Computing Conference (2004).
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
