ML based career suggestive system for informal job sector considering cognitive skills
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
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| Langue: | English |
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Brac University
2023
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| Accès en ligne: | http://hdl.handle.net/10361/18003 |
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10361-18003 |
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10361-180032023-03-22T21:01:48Z ML based career suggestive system for informal job sector considering cognitive skills Tonny, Ms. Ayesha Siddika Hafsa Lavlu, Md. Tousif Hasan Ghosh, Abhijit Kumar Roy, Sourojit Rahman, Md. Khalilur Shakil, Shifur Rahman Department of Computer Science and Engineering, Brac University Career Capacity Interests Cognitive skills Informal sectors Extreme Gradient Boosting (XGB) Random Forest Classifier MinMaxScaler Pymetrics Machine learning Artificial intelligence This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 41-42). We are striving to build a realistic procedure by which we, particularly the future generation, will be able to choose the right career based on their capacity and interests. A few well-known international firms, including IBM, Unilever, LinkedIn, Accenture, and others, utilize Pymetrics to hire their staff, which is based on cognitive skills in the formal sector. Our work, however, is the first in the informal sector. On our primary collected dataset, we used six distinct algorithms, including Logistic Regression, Decision Tree, Random Forest Classifier, Support Vector Classification, Multilayer Perceptron Classifier, and Extreme Gradient Boosting (XGB), and discovered that Random Forest Classifier and Extreme Gradient Boosting (XGB) are the best for this system, with the accuracy of 57% and 60%, respectively. We’ve also used MinMaxScaler to enhance our output. After that, we observed that the Random Forest Classifier approach had a nearly 62% higher accuracy. The Extreme Gradient Boosting (XGB) approach, on the other hand, has a precision of 58.6%. After completing our evaluation, we opted to use the Random Forest Classifier for our system instead of MinMaxScaler. Based on these insights, we’ll match individuals with employment, smoothing out labor market inefficiencies and leading to considerable boosts in productivity, income, and well-being. Ms. Ayesha Siddika Tonny Hafsa Md. Tousif Hasan Lavlu Abhijit Kumar Ghosh Sourojit Roy B. Computer Science 2023-03-22T06:35:35Z 2023-03-22T06:35:35Z 2022 2022-05 Thesis ID 18301197 ID 18301205 ID 18301190 ID 18301191 ID 18301199 http://hdl.handle.net/10361/18003 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 42 pages application/pdf Brac University |
| institution |
Brac University |
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Institutional Repository |
| language |
English |
| topic |
Career Capacity Interests Cognitive skills Informal sectors Extreme Gradient Boosting (XGB) Random Forest Classifier MinMaxScaler Pymetrics Machine learning Artificial intelligence |
| spellingShingle |
Career Capacity Interests Cognitive skills Informal sectors Extreme Gradient Boosting (XGB) Random Forest Classifier MinMaxScaler Pymetrics Machine learning Artificial intelligence Tonny, Ms. Ayesha Siddika Hafsa Lavlu, Md. Tousif Hasan Ghosh, Abhijit Kumar Roy, Sourojit ML based career suggestive system for informal job sector considering cognitive skills |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Rahman, Md. Khalilur |
| author_facet |
Rahman, Md. Khalilur Tonny, Ms. Ayesha Siddika Hafsa Lavlu, Md. Tousif Hasan Ghosh, Abhijit Kumar Roy, Sourojit |
| format |
Thesis |
| author |
Tonny, Ms. Ayesha Siddika Hafsa Lavlu, Md. Tousif Hasan Ghosh, Abhijit Kumar Roy, Sourojit |
| author_sort |
Tonny, Ms. Ayesha Siddika |
| title |
ML based career suggestive system for informal job sector considering cognitive skills |
| title_short |
ML based career suggestive system for informal job sector considering cognitive skills |
| title_full |
ML based career suggestive system for informal job sector considering cognitive skills |
| title_fullStr |
ML based career suggestive system for informal job sector considering cognitive skills |
| title_full_unstemmed |
ML based career suggestive system for informal job sector considering cognitive skills |
| title_sort |
ml based career suggestive system for informal job sector considering cognitive skills |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/18003 |
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