Trend analysis of university placement by using machine learning algorithms

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Machine learning is a method of data analysis that automates analytical model building. These models help you to make a trend analysis of university placements data, to predict a placement rate for the students of an upcoming year which will help the university to analyze the performance during placements. Many students look at universities as a means of investment which can help them make a great future by getting placed in good companies and which will relieve their stress and unease from their lives before graduating from the university. The trend will also help in giving the companies reasons as to why they should visit university again and again. Some attributes play the very important role while analyzing the student for e.g. Student’s name, Department, Company, Location and Annual package. So, classification can help you to classify those data and clustering helps to make the clusters department wise. In this paper we have used neural networks to predict the upcoming student placement and got 77% of accuracy while testing were iteration are 1000. Through extensive trend analysis of varies complex data collected from different sources, we can demonstrate that our analysis can provide a good pragmatic solution for future placement of students.


  • Keywords

    Support Vector Machine; Decision Tree and Artificial Neural Network.

  • References

      [1] Mangasuli Sheetal B1, Prof. Savita Bakare2 “Prediction of Campus Placement Using Data Mining Algorithm-Fuzzy logic and K nearest neighbour” International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 6, June 2016

      [2] Ajay Kumar Pal Research Scholar, Sai Nath University, Ranchi, Jharkhand ,Saurabh Pal Head, Department of MCA, VBS Purvanchal University, Jaunpur, India“classification model of prediction for placement of students” I.J.Modern Education and Computer Science, 2013, 11, 49-56 Published Online in MECS ( DOI: 10.5815/ijmecs.2013.11.07

      [3] Namita Puri , Deepali Khot , Pratiksha Shinde , Kishori Bhoite , Prof. Deepali Maste,Department of Computer Engineering, Atharva College of Engineering, Malad,Maharashtra,India “Student Placement Prediction Using ID3 Algorithm” International Journal for Research in Applied Science & Engineering Technology Volume 3 Issue III, March 2015

      [4] Amanpreet Singh, Narina Thakur, Aakanksha Sharma “A Review of Supervised Machine Learning Algorithms” IEEE 978-9-3805-4421-2/16/$31.00c 2016

      [5] ELSEVIER “A comparison of machine learning techniques for customer churn prediction” T. Vafeiadis a, K.I. Diamantaras b, G. Sarigiannidis a, K.Ch. Chatzisavvas International Conference on Communication Technology and System Design 2011

      [6] H.B. Kazemian , S. Ahmed ELSEVIER “Comparisons of machine learning techniques for detecting malicious a Webpages”

      [7] Elaf Abu Amrieh Thair Hamtini “ Mining Educational Data to Predict Student's academic Performance using Ensemble Methods” International Journal of Database Theory and Application Vol.9, No.8 (2016), pp.119-136 ,2016

      [8] Seema Sharma1, Jitendra Agrawal2, Shikha Agarwal3, Sanjeev Sharma4 “Machine Learning Techniques for Data Mining: A Survey” IEEE International Conference on Computational Intelligence and Computing Research, 978-1-4799-1597-2/13/$31.00 ©2013

      [9] Ajay Shiv Sharma1, Swaraj Prince2, Shubham Kapoor3 Keshav Kumar4 “PPS - Placement Prediction System using Logistic Regression” IEEE 978-1-4799-6876-3/14/$31.00c 2014

      [10] Madan Somvanshi, Shital Tambade, Pranjali Chavan, S.V. Shinde “A Review of Machine Learning Techniques using Decision Tree and Support Vector Machine” IEEE International Conference on Computational Intelligence and Computing Research 2016

      [11] Rakesh Kumar Arora Dr. Dharmendra Badal “ Placement Prediction through Data Mining” International Journal of Advanced Research in Computer Science and Software Engineering Volume 4, Issue 7, July 2014

      [12] Pedro Strecht Luís Cruz Carlos Soares João Mendes-Moreira Rui Abreu “A Comparative Study of Classification and Regression Algorithms for Modelling Students’ Academic Performance” ” IEEE 978-1-4799-6876-3/14/$31.00c 2015

      [13] J. Bucko*, L. Kakalejčik “Machine Learning Techniques in the Education Process of Students of Economics” MIPRO 2017, May 22- 26, 2017, Opatija, Croatia

      [14] M. Mayilvaganan D. Kalpanadevi “Comparison of Classification Techniques for predicting the performance of Students Academic Environment” International Conference on Communication and Network Technologies (ICCNT) 2014




Article ID: 13034
DOI: 10.14419/ijet.v7i2.4.13034

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.