Employability Analysis using Data Mining

Authors

  • Shubham Kambli  B.E. Student Department of Information Technology, Vidyalankar Institute of Technology, Mumbai, India
  • Shravani Joshi  Assistant Professor Department of Information Technology, Vidyalankar Institute of Technology, Mumbai, India
  • Gaurav Chauhan  Assistant Professor Department of Information Technology, Vidyalankar Institute of Technology, Mumbai, India
  • Deepali Nayak  

Keywords:

Data Mining, Employability Analysis, Recommendation

Abstract

In today's competitive world, it's getting difficult to get a job of suitable profile. It may be because of automation, recession or maybe the students themselves are not knowledgeable enough for the work expected from a particular profile. It can also be that students aren’t aware of what the requirements are to work in a particular domain. Our system has been created to help students in all such scenarios. From a few assessment tests, it can provide you with an employability factor which shows how much employable you are for a particular domain, and also recommend courses, certifications to work on your weaknesses and gain an edge over others in the field. It can also suggest jobs which suit your current overall profile. This system can be used by students to assess themselves, by placement cells to measure employability of their student, and also by companies to check employability potential of prospective employees.

References

  1. Pratiyush Guleria and Manu Sood, "Predicting Student Placements using Bayesian Classification," presented at the Third International Conference on Image Infonnation Processing, Waknaghat, India, 2015.
  2. Mansi Gera and Shivani Goel, "A Model for Predicting the Eligibility for Placement of Students Using Data Mining Technique," presented at the International Conference on Computing, Communication and Automation (ICCCA2015), Noida, India, 2015.
  3. Tripti Mishra and Dharmiinder Kumar, "Mining Students Data for Performance Prediction," presented at theFourth International Conference on Advanced Computing and Communication Technologies, Rohtak, India, 2014.
  4. Jaime Ramirez Castillo and Aldabbagh Ghadah and Habib M. Fardoun," Towards improved student placement and preparation methods on IT post-secondary education," in Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, Krako, Poland, 2013.
  5. Cheng Lei and Kin Fun Li," Academic Performance Predictors," presented at the 29th International Conference on Advanced Information Networking and Applications Workshops, Gwangiu, South Korea, 2015.
  6. Ajay Shiv Sharma and Swaraj Prince and Shubham Kapoor and Keshav Kumar," PPS - Placement Prediction System using Logistic Regression," presented at the IEEE International Conference on MOOC, Innovation and Technology in Education (MITE), Patiala, India, 2014.
  7. Animesh Giri and M.Vignesh and V. Bhagavath and Bysani Pruthv and Naini Dubey," A Placement Prediction System Using K-Nearest Neighbors Classifier," presented at the Second International Conference on Cognitive Computing and Information Processing (CCIP), Mysore, India, 2016.
  8. Karan Pruthi and Parteek Bhatia," Application of Data Mining in predicting placement of Students," presented at the International Conference on Green Computing and Internet of Things (ICGCloT), Noida, India, 2015.
  9. Omar Augusto and Echegaray-Calderon and Dennis Barrios-Aranibar," Optimal selection of factors using Genetic Algorithms and Neural Networks for the prediction of students’ academic performance," Computational Intelligence (LA-CCI),2015 Latin America Congress, Curitiba, Brazil, 2015.
  10. Tismy Devasia and Vinushree T.P. and Vinayak Hegde," Prediction of Students Performance using Educational Data Mining," presented at the Data Mining and Advanced Computing (SAPIENCE), International Conference, Ernakulam, India, 2016.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2018-04-30

Issue

Volume 3, Issue 3, March-April-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Shubham Kambli, Shravani Joshi, Gaurav Chauhan, Deepali Nayak, " Employability Analysis using Data Mining, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.812-817, March-April-2018.