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Job position prediction based on skills and experience using machine learning algorithm / Ezaryf Hamdan

In response to the intensifying competition in the job market, job seekers often grapple with the challenge of identifying the most suitable positions based on their skills and experience. This paper proposes a sophisticated Job Position Prediction system utilizing Machine Learning algorithms and le...

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Main Author: Hamdan, Ezaryf
Format: Thesis
Language:English
Published: 2024
Subjects:
Expert systems (Computer science). Fuzzy expert systems
Online Access:https://ir.uitm.edu.my/id/eprint/95550/1/95550.pdf
https://ir.uitm.edu.my/id/eprint/95550/
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spelling my.uitm.ir.955502024-05-31T02:52:41Z https://ir.uitm.edu.my/id/eprint/95550/ Job position prediction based on skills and experience using machine learning algorithm / Ezaryf Hamdan Hamdan, Ezaryf Expert systems (Computer science). Fuzzy expert systems In response to the intensifying competition in the job market, job seekers often grapple with the challenge of identifying the most suitable positions based on their skills and experience. This paper proposes a sophisticated Job Position Prediction system utilizing Machine Learning algorithms and leveraging data from LinkedIn profiles. The objective is to develop an innovative and user-friendly platform offering accurate job position predictions to aid job seekers in finding optimal career opportunities. The proposed system integrates data processing modules to preprocess and analyse LinkedIn job posting pages, extracting crucial information such as job titles, company names, years of experience, qualifications, and skills. Text preprocessing ensures consistent data representation and facilitates validation. The Machine Learning algorithm, comprising Random Forest, Linear Regression, XGBoost, SVM, and Stacking Ensemble, is embedded in the system for job position predictions based on the analysed data. The algorithm undergoes rigorous training with a vast dataset to ensure high prediction accuracy and reliability. Accessible through a desktop application, the Job Position Prediction System prioritizes userfriendliness for job seekers. Users input their job skills and years of experience, receiving personalized job position predictions as the system analyses the input and provides the most suitable job positions based on LinkedIn data. The success of this project is evaluated using various metrics, including prediction accuracy, precision, recall, and F1 score. Cross-validation techniques are employed to validate the model's performance and ensure robustness. The development and evaluation of the prediction system adhere to a comprehensive research methodology encompassing data understanding, description, preprocessing, feature extraction, and model evaluation. This research culminates in the creation of an efficient and accurate Job Position Prediction System, empowering job seekers with valuable insights to enhance their job search process. 2024 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/95550/1/95550.pdf Job position prediction based on skills and experience using machine learning algorithm / Ezaryf Hamdan. (2024) Degree thesis, thesis, Universiti Teknologi MARA, Terengganu.
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Expert systems (Computer science). Fuzzy expert systems
spellingShingle Expert systems (Computer science). Fuzzy expert systems
Hamdan, Ezaryf
Job position prediction based on skills and experience using machine learning algorithm / Ezaryf Hamdan
description In response to the intensifying competition in the job market, job seekers often grapple with the challenge of identifying the most suitable positions based on their skills and experience. This paper proposes a sophisticated Job Position Prediction system utilizing Machine Learning algorithms and leveraging data from LinkedIn profiles. The objective is to develop an innovative and user-friendly platform offering accurate job position predictions to aid job seekers in finding optimal career opportunities. The proposed system integrates data processing modules to preprocess and analyse LinkedIn job posting pages, extracting crucial information such as job titles, company names, years of experience, qualifications, and skills. Text preprocessing ensures consistent data representation and facilitates validation. The Machine Learning algorithm, comprising Random Forest, Linear Regression, XGBoost, SVM, and Stacking Ensemble, is embedded in the system for job position predictions based on the analysed data. The algorithm undergoes rigorous training with a vast dataset to ensure high prediction accuracy and reliability. Accessible through a desktop application, the Job Position Prediction System prioritizes userfriendliness for job seekers. Users input their job skills and years of experience, receiving personalized job position predictions as the system analyses the input and provides the most suitable job positions based on LinkedIn data. The success of this project is evaluated using various metrics, including prediction accuracy, precision, recall, and F1 score. Cross-validation techniques are employed to validate the model's performance and ensure robustness. The development and evaluation of the prediction system adhere to a comprehensive research methodology encompassing data understanding, description, preprocessing, feature extraction, and model evaluation. This research culminates in the creation of an efficient and accurate Job Position Prediction System, empowering job seekers with valuable insights to enhance their job search process.
format Thesis
author Hamdan, Ezaryf
author_facet Hamdan, Ezaryf
author_sort Hamdan, Ezaryf
title Job position prediction based on skills and experience using machine learning algorithm / Ezaryf Hamdan
title_short Job position prediction based on skills and experience using machine learning algorithm / Ezaryf Hamdan
title_full Job position prediction based on skills and experience using machine learning algorithm / Ezaryf Hamdan
title_fullStr Job position prediction based on skills and experience using machine learning algorithm / Ezaryf Hamdan
title_full_unstemmed Job position prediction based on skills and experience using machine learning algorithm / Ezaryf Hamdan
title_sort job position prediction based on skills and experience using machine learning algorithm / ezaryf hamdan
publishDate 2024
url https://ir.uitm.edu.my/id/eprint/95550/1/95550.pdf
https://ir.uitm.edu.my/id/eprint/95550/
_version_ 1800726759019118592
score 13.211869

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