A survey on Predictive Analysis in Employment Trends

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    This paper addresses the theories of using predictive analysis and Data Mining in arriving at suitable patterns and predicting paths and trends in the current Employment Scenario more specifically to the Engineering sector. India produces 1.5 million engineers every year, and yet there is a significant gap between their skills and the jobs and corresponding salaries they are offered. Recognizing the factors that influence this gap can help us bridge it. The survey shows that the ideal route to doing so, is by employing various Predictive analysis and Data Mining techniques on appropriate data sets, which help in addressing these issues. As per the survey, appropriate visualization techniques have also been used to extract the meaning from the prediction and analysis.

     

     


  • Keywords


    Predictive Analysis, Employability, Data Mining.

  • References


      [1] “The skills gap and what it means for your business,” www.financialexpress.com/article/industry/jobs/the-skills-gap-andwhat-it-means-for-your-business/138700, accessed: 2016-01-29.

      [2] “Predictive Analysis and Data Mining among the Employment of Fresh Graduate Students in HEI”

      [3] Nor Azziaty Abdul Rahman, Kian Lam Tan, Chen Kim Lim, https://doi.org/10.1063/1.5005340

      [4] Xu, W., Li, Z., Cheng, C., & Zheng, T. (2012). Data mining for unemployment rate prediction using search engine query data. Service Oriented Computing and Applications, 7(1), 33–42. https://doi.org/10.1007/s11761- 012-0122-2

      [5] Mishra, T. (2016). Students’ Employability Prediction Model through Data Mining, 11(4), 2275–2282.

      [6] Sapaat, M. A., Mustapha, A., Ahmad, J., &Chamili, K. (2011). A Data Mining Approach to Construct Graduates Employability Model in Malaysia, 1(4), 1086–1098

      [7] Job and Candidate Recommendation with Big Data Support: A Contextual Online Learning Approach., GLOBECOM 2017 - 2017 IEEE Global Communications Conference

      [8] Xianyin Li, Wanming Chen (2009). A Grey –Markov Predication for unemployment rate of graduates in China

      [9] Aziz, A. A., Ismail, N. H., Ahmad, F., & Hassan, H. (2015). A framework for students’ academic performance analysis using naive bayes classifier. JurnalTeknologi, 75(3), 13–19. https://doi.org/10.11113/jt.v75.5037

      [10] Student Academic Performance and Social Behavior Predictor using Data Mining Techniques, Computing, Communication and Automation (ICCCA), 2017 International Conference on

      [11] Gao, L. (2015). Analysis of Employment Data Mining for University Student based on Weka Platform, 2(4), 130–133

      [12] Jantawan, B., & Tsai, C. (2013). The Application of Data Mining to Build Classification Model for Predicting Graduate Employment. International Journal of Computer Science and Information Security, 11(10), 1–8. https://doi.org/10.1016/j.bdr. 2015.01.001

      [13] Chih-Chou Chiu' and Chao-Ton Su2. (2014). A Novel Neural Network Model Using Box-Jenkins Technique and Response Surface Methodology to Predict Unemployment Rate

      [14] Dr. Yashpal Singh, Alok Singh Chauhan, Neural Networks in Data Mining.

      [15] T. Padmapriya and V. Saminadan, “Inter-cell Load Balancing Technique for Multi- class Traffic in MIMO - LTE - A Networks”, International Conference on Advanced Computer Science and Information Technology , Singapore, vol.3, no.8, July 2015.

      [16] S.V.Manikanthan and K.srividhya "An Android based secure access control using ARM and cloud computing", Published in: Electronics and Communication Systems (ICECS), 2015 2nd International Conference on 26-27 Feb. 2015, Publisher: IEEE,DOI:10.1109/ECS.2015.7124833.


 

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Article ID: 12082
 
DOI: 10.14419/ijet.v7i2.24.12082




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