Prediction of Potential Future IT Personnel in Bangladesh using Machine Learning Classifier

  • Md. Hasnat Parvez Lecturer, Department of Computer Science and Engineering, Gono University, Savar, Dhaka, BANGLADESH
  • Most. Moriom Khatun Sher-e-Bangla Agricultural University, Dhaka, BANGLADESH
  • Sayed Mohsin Reza Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, BANGLADESH
  • Md. Mahfujur Rahman Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, BANGLADESH
  • Md. Fazlul Karim Patwary Professor, Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, BANGLADESH
Keywords: Future IT Personnel, IT in Developing Country, Machine Learning Classifier

Abstract

Bangladesh is one of the most promising developing countries in IT sector, where people from several disciplines and experiences are involved in this sector. However, no direct analysis in this sector is published yet, which covers the proper guideline for predicting future IT personnel. Hence this is not a simple solution, training data from real IT sector are needed and trained several classifiers for detecting perfect results. Machine learning algorithms can be used for predicting future potential IT personnel. In this paper, four different classifiers named as Naive Bayes, J48, Bagging and Random Forest in five different folds are experimented for that prediction. Results are pointed out that Random Forest performs better accuracy than other experimented classifier for future IT personnel prediction. It is mentioned that the standard accuracy measurement process named as Precision, Recall, F-Measure, ROC Area etc. are used for evaluating the results.

 

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Published
2017-09-15
How to Cite
Parvez, M. H., Khatun, M. M., Reza, S. M., Rahman, M. M. and Patwary, M. F. K. (2017) “Prediction of Potential Future IT Personnel in Bangladesh using Machine Learning Classifier”, Global Disclosure of Economics and Business, 6(1), pp. 7-18. Available at: http://i-proclaim.my/archive/index.php/gdeb/article/view/306 (Accessed: 14October2019).
Section
Articles