An allotment of H1B work visa in USA using machine learning

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    H1B work visas are utilized to contract profoundly talented outside specialists at low wages in America which help firms and impact U.S economy unfavorably. In excess of 100,000 individuals for every year apply tight clamp for higher examinations and also to work and number builds each year. Selections of foreigners are done by lottery system which doesn’t follow any full proofed method and so results cause a loophole between US-based and foreign workers. We endeavor to examine petitions filled from 2015 to 2017 with the goal that a superior prediction model need to develop using machine learning which helps to foresee the aftereffect of the request of ahead of time which shows whether an appeal to is commendable or not. In this work, we use seven classification models Decision tree, C5.0, Random Forest, Naïve Bayes, Neural Network and SVM which predict the status of a petition as certified, denied, withdrawal or certified with-drawls. The predictions of these models are checked on accuracy parameter. It is found that C5.0 outperform with the best accuracy of 94.62 as a single model but proposed model gives better results of 95.4 accuracies which is built by machine ensemble method and this is validated by 10 fold cross-validation.

     


  • Keywords


    H1b; Decision Tree; Random Forest; Naïve Bayes; Neural Network; C5.0; SVM.

  • References


      [1] Dhanasekar Sundararaman , Nabarun Pal , Aashish Kumar Misraa ,(2017),” An analysis of nonimmigrant work visas in the USA using Machine Learning” ”, International Journal of Computer Science and Security(IJCSS), Vol. 6,

      [2] https://www.foreignlaborcert.doleta.gov/performancedata.cfm.

      [3] UNITED STATES DEPARTMENT OF LABOR. (2009, Janu-ary 15). OFLC Performance Data. (www.dol.gov) Retrieved September 09, 2017, from UNITED STATES DEPARTMENT OF LABOR Employment & Training Administration:

      [4] Trim Bach, S., (2016), Giving the Market a Microphone: Solu-tions to the Ongoing Displacement of US Workers through the H1B Visa Program. Nw. J. Int'l L. & Bus., 37, p.275.

      [5] Doran, K., Gelber, A. and Isen, A., 2014. The effects of high-skilled immigration policy on firms: Evidence from H-1B visa lotteries (No. w20668). National Bureau of Economic Re-search. https://doi.org/10.3386/w20668.

      [6] Bound, J., Khanna, G. and Morales, N., (2017). Understanding the Economic Impact of the H-1B Program on the US. In High-Skilled Migration to the United States and its Economic Con-sequences. University of Chicago Press.

      [7] Mithas, S. and Lucas Jr, H.C., 2010. Are foreign IT workers cheaper? US visa policies and compensation of information technology professionals. Management Science, 56(5), pp.745-765. https://doi.org/10.1287/mnsc.1100.1149.

      [8] Kaggle H-1B dataset, https://www.kaggle.com/nsharan/h-1b-visa 13 outsourcing companies took nearly one-third of all H-1B visas in 2014, https://www.nytimes.com/interactive/2015/11/06/us/outsourcing-companies-dominate-h1b-visas.html.

      [9] Disney 'forced 250 of its American IT workers to train up the Indian workers who replaced them’, http://www.dailymail.co.uk/news/article-4037392/Disney-fired-250-American-workers-replaced-Indian-staff-visas-suit-says.html. 2016

      [10] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V. and Vanderplas, J., Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(Oct), pp.2825-2830, 2011

      [11] Qing-yun Dai, Chun-ping Zhang, Hao Wu, “Research of Decision Tree Classification Algorithm in Data Mining”, Vol.9, No.5 (2016), pp.1-8

      [12] J.Ross Quinlan, “C4.5: Programs for machine learning”, Elsevier, 2014.

      [13] Sohag Sundar Nanda, Soumya Mishra, Sanghamitra Mohan-ty, Oriya Language Text Mining Using C5.0 Algorithm, (IJCSIT) International Journal of Computer Science and In-formation Technologies, Vol. 2 (1) , 2011

      [14] PANG Su-lin, GONG Ji-zhang C5.0 Classification Algorithm and Application on Individual Credit Evaluation of Banks; Volume 29, Issue 12, February 2009.

      [15] Sona Taheri, Musa Mammadov, Learning the Naive Bayes Classifier with Optimization Models, Vol. 23, No. 4, 787–795, 2013.

      [16] Chang, C. and Lin, C. (2001). LIBSVM: A library for sup-port vector machines, http://www.csie.ntu.edu.tw/ cjlin/libsvm.

      [17] S.S. Keerthi and E.G. Gilbert. Convergence of a generalized SMO algorithm for SVM classifier design. Machine Learning, 46(1):351–360, 2002. https://doi.org/10.1023/A:1012431217818.

      [18] CTomM.Mitchel, McGrawHil, Decision Tree Learning, Lec-ture slides for textbook Machine Learning,, 197

      [19] JürgenSchmidhuber, “Deep learning in neural networks: An overview”, Elsevier, Volume 61, January 2015, Pages 85-117.

      [20] Gao Huang, Guang-Bin Huang, Shiji Song, Keyou You, “Trends in extreme learning machines: A review”, Elsevier, Volume 61, January 2015, Pages 32-48.

      [21] Andy Liaw and Matthew Wiener,” Classification and Re-gression by random Forest”. R News, 2(3):18–22, 2002.

      [22] Arun Pretorius, Surette Bierman, Sarel J.steel (2017.).” A meta-analysis of research in random forests for classifica-tion” IEEE Conference, 16 January.

      [23] Eesha Goel, Er. Abhilasha, “Random Forest: A Review”, ijarcsse, Volume 7, Issue 1, January 2017.

      [24] L. Breiman, “Random Forest”. October 2001, Volume 45, Issue 1, pp. 5–32 45

      [25] Christian Heumann, Michael Schomaker, Shalabh (2016), “Introduction of statistics and data analysis”, Springer.

      [26] C. Burges, (1998.), “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Dis-covery, Kluwer Academic Publishers.

      [27] A. Liaw, M. Wiener, “Classification and regression by ran-dom forest”, R news 2 (3), (2002) 18–22.

      [28] Nahla H. Barakat and Andrew P. Bradley,”Rule Extraction from Support Vector Machines: A Sequential Covering Ap-proach”, VOL. 19, NO. 6, JUNE 2007.

      [29] Guo-Xun Yuan; Chia-Hua Ho; Chih-Jen Lin "Recent Ad-vances of Large-Scale Linear Classification". IEEE. 100, (2012).

      [30] C. K. Williams, A. Engelhardt, T. Cooper,Z. Mayer, A. Ziem, L. Scrucca, Y. Tang, C. Candan, M. M. Kuhn, Package caret.

      [31] V. W. Aalst, “Exterminating the Dynamic Change Bug: A Concrete Approach to Support Workflow Change”, Eindho-ven, UK: Eindhoven University of Technology, (2000).

      [32] S. Rinderle, M. Reichert and P. Dadam, “Correctness Criteria for Dynamic Changes in Workflow Systems”, Data & Knowledge Engineering, vol. 50, no. 1, pp. 9-34. https://doi.org/10.1016/j.datak.2004.01.002.


 

View

Download

Article ID: 12642
 
DOI: 10.14419/ijet.v7i2.27.12642




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