Job Market Cheat Codes: Prototyping Salary Prediction and Job Grouping with Synthetic Job Listings
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908413211967488 |
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| author | Alsheyab, Abdel Rahman Alkhasawneh, Mohammad Shahin, Nidal |
| author_facet | Alsheyab, Abdel Rahman Alkhasawneh, Mohammad Shahin, Nidal |
| contents | This paper presents a machine learning methodology prototype using a large synthetic dataset of job listings to identify trends, predict salaries, and group similar job roles. Employing techniques such as regression, classification, clustering, and natural language processing (NLP) for text-based feature extraction and representation, this study aims to uncover the key features influencing job market dynamics and provide valuable insights for job seekers, employers, and researchers. Exploratory data analysis was conducted to understand the dataset's characteristics. Subsequently, regression models were developed to predict salaries, classification models to predict job titles, and clustering techniques were applied to group similar jobs. The analyses revealed significant factors influencing salary and job roles, and identified distinct job clusters based on the provided data. While the results are based on synthetic data and not intended for real-world deployment, the methodology demonstrates a transferable framework for job market analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15879 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Job Market Cheat Codes: Prototyping Salary Prediction and Job Grouping with Synthetic Job Listings Alsheyab, Abdel Rahman Alkhasawneh, Mohammad Shahin, Nidal Machine Learning This paper presents a machine learning methodology prototype using a large synthetic dataset of job listings to identify trends, predict salaries, and group similar job roles. Employing techniques such as regression, classification, clustering, and natural language processing (NLP) for text-based feature extraction and representation, this study aims to uncover the key features influencing job market dynamics and provide valuable insights for job seekers, employers, and researchers. Exploratory data analysis was conducted to understand the dataset's characteristics. Subsequently, regression models were developed to predict salaries, classification models to predict job titles, and clustering techniques were applied to group similar jobs. The analyses revealed significant factors influencing salary and job roles, and identified distinct job clusters based on the provided data. While the results are based on synthetic data and not intended for real-world deployment, the methodology demonstrates a transferable framework for job market analysis. |
| title | Job Market Cheat Codes: Prototyping Salary Prediction and Job Grouping with Synthetic Job Listings |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.15879 |