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

Global Disclosure of Economics and Business

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Title Prediction of Potential Future IT Personnel in Bangladesh using Machine Learning Classifier
Creator Parvez, Md. Hasnat
Khatun, Most. Moriom
Reza, Sayed Mohsin
Rahman, Md. Mahfujur
Patwary, Md. Fazlul Karim
Subject Future IT Personnel, IT in Developing Country, Machine Learning Classifier
Description 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. 
Publisher i-Proclaim
Date 2017-09-15
Type info:eu-repo/semantics/article
Peer-reviewed Article
Format application/pdf
Source Global Disclosure of Economics and Business; Vol 6, No 1 (2017): 11th Issue; 7-18
Language eng
Rights Copyright (c) 2017 Md. Hasnat Parvez, Most. Moriom Khatun, Sayed Mohsin Reza, Md. Mahfujur Rahman, Md. Fazlul Karim Patwary

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