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

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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Global Disclosure of Economics and Business 2017-06, Vol.6 (1), p.7-18
Hauptverfasser: Parvez, Md. Hasnat, Khatun, Most. Moriom, Reza, Sayed Mohsin, Rahman, Md. Mahfujur, Patwary, Md. Fazlul Karim
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 18
container_issue 1
container_start_page 7
container_title Global Disclosure of Economics and Business
container_volume 6
creator Parvez, Md. Hasnat
Khatun, Most. Moriom
Reza, Sayed Mohsin
Rahman, Md. Mahfujur
Patwary, Md. Fazlul Karim
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.
doi_str_mv 10.18034/gdeb.v6i1.112
format Article
fullrecord <record><control><sourceid>crossref</sourceid><recordid>TN_cdi_crossref_primary_10_18034_gdeb_v6i1_112</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>10_18034_gdeb_v6i1_112</sourcerecordid><originalsourceid>FETCH-crossref_primary_10_18034_gdeb_v6i1_1123</originalsourceid><addsrcrecordid>eNqVz8FKw0AQBuClKLTYXj3PCyTuJk3dXC0WBYUcevOwbJNJOrDOymwi-PY2xRfwND8DPz-fUvdG58bqcvswdHjKv3dkcmOKhVoVpX7M6qoubq65ymqzs0u1SYlO2pRWV7W1K_XRCHbUjhQZYg9NHJFH8gEO0zgJwusRGpQUmTEAMTx5HoLvMJ1hSsQDvPv2TIzwhl54fuyDv2z0hLJWt70PCTd_907lh-fj_iVrJaYk2LsvoU8vP85od1W4WeFmhbsoyn8XfgEqo1IR</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Prediction of Potential Future IT Personnel in Bangladesh using Machine Learning Classifier</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Parvez, Md. Hasnat ; Khatun, Most. Moriom ; Reza, Sayed Mohsin ; Rahman, Md. Mahfujur ; Patwary, Md. Fazlul Karim</creator><creatorcontrib>Parvez, Md. Hasnat ; Khatun, Most. Moriom ; Reza, Sayed Mohsin ; Rahman, Md. Mahfujur ; Patwary, Md. Fazlul Karim</creatorcontrib><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.</description><identifier>ISSN: 2305-9168</identifier><identifier>EISSN: 2307-9592</identifier><identifier>DOI: 10.18034/gdeb.v6i1.112</identifier><language>eng</language><ispartof>Global Disclosure of Economics and Business, 2017-06, Vol.6 (1), p.7-18</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-crossref_primary_10_18034_gdeb_v6i1_1123</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Parvez, Md. Hasnat</creatorcontrib><creatorcontrib>Khatun, Most. Moriom</creatorcontrib><creatorcontrib>Reza, Sayed Mohsin</creatorcontrib><creatorcontrib>Rahman, Md. Mahfujur</creatorcontrib><creatorcontrib>Patwary, Md. Fazlul Karim</creatorcontrib><title>Prediction of Potential Future IT Personnel in Bangladesh using Machine Learning Classifier</title><title>Global Disclosure of Economics and Business</title><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.</description><issn>2305-9168</issn><issn>2307-9592</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNqVz8FKw0AQBuClKLTYXj3PCyTuJk3dXC0WBYUcevOwbJNJOrDOymwi-PY2xRfwND8DPz-fUvdG58bqcvswdHjKv3dkcmOKhVoVpX7M6qoubq65ymqzs0u1SYlO2pRWV7W1K_XRCHbUjhQZYg9NHJFH8gEO0zgJwusRGpQUmTEAMTx5HoLvMJ1hSsQDvPv2TIzwhl54fuyDv2z0hLJWt70PCTd_907lh-fj_iVrJaYk2LsvoU8vP85od1W4WeFmhbsoyn8XfgEqo1IR</recordid><startdate>20170630</startdate><enddate>20170630</enddate><creator>Parvez, Md. Hasnat</creator><creator>Khatun, Most. Moriom</creator><creator>Reza, Sayed Mohsin</creator><creator>Rahman, Md. Mahfujur</creator><creator>Patwary, Md. Fazlul Karim</creator><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20170630</creationdate><title>Prediction of Potential Future IT Personnel in Bangladesh using Machine Learning Classifier</title><author>Parvez, Md. Hasnat ; Khatun, Most. Moriom ; Reza, Sayed Mohsin ; Rahman, Md. Mahfujur ; Patwary, Md. Fazlul Karim</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-crossref_primary_10_18034_gdeb_v6i1_1123</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><toplevel>online_resources</toplevel><creatorcontrib>Parvez, Md. Hasnat</creatorcontrib><creatorcontrib>Khatun, Most. Moriom</creatorcontrib><creatorcontrib>Reza, Sayed Mohsin</creatorcontrib><creatorcontrib>Rahman, Md. Mahfujur</creatorcontrib><creatorcontrib>Patwary, Md. Fazlul Karim</creatorcontrib><collection>CrossRef</collection><jtitle>Global Disclosure of Economics and Business</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Parvez, Md. Hasnat</au><au>Khatun, Most. Moriom</au><au>Reza, Sayed Mohsin</au><au>Rahman, Md. Mahfujur</au><au>Patwary, Md. Fazlul Karim</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of Potential Future IT Personnel in Bangladesh using Machine Learning Classifier</atitle><jtitle>Global Disclosure of Economics and Business</jtitle><date>2017-06-30</date><risdate>2017</risdate><volume>6</volume><issue>1</issue><spage>7</spage><epage>18</epage><pages>7-18</pages><issn>2305-9168</issn><eissn>2307-9592</eissn><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.</abstract><doi>10.18034/gdeb.v6i1.112</doi></addata></record>
fulltext fulltext
identifier ISSN: 2305-9168
ispartof Global Disclosure of Economics and Business, 2017-06, Vol.6 (1), p.7-18
issn 2305-9168
2307-9592
language eng
recordid cdi_crossref_primary_10_18034_gdeb_v6i1_112
source Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
title Prediction of Potential Future IT Personnel in Bangladesh using Machine Learning Classifier
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-10T17%3A48%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-crossref&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Prediction%20of%20Potential%20Future%20IT%20Personnel%20in%20Bangladesh%20using%20Machine%20Learning%20Classifier&rft.jtitle=Global%20Disclosure%20of%20Economics%20and%20Business&rft.au=Parvez,%20Md.%20Hasnat&rft.date=2017-06-30&rft.volume=6&rft.issue=1&rft.spage=7&rft.epage=18&rft.pages=7-18&rft.issn=2305-9168&rft.eissn=2307-9592&rft_id=info:doi/10.18034/gdeb.v6i1.112&rft_dat=%3Ccrossref%3E10_18034_gdeb_v6i1_112%3C/crossref%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true