A Multiple Linear Regression Approach to Predicting AI Professionals’
Salaries from Location and Skill Data
Dublin Core
Title
A Multiple Linear Regression Approach to Predicting AI Professionals’
Salaries from Location and Skill Data
Salaries from Location and Skill Data
Subject
AI Salary Prediction; Multiple Linear Regression; Experience Level; Geographic Location; Technical Skills
Description
The rapid growth of Artificial Intelligence (AI) industries worldwide has increased the demand for skilled professionals and highlighted the need to understand
salary determinants in this sector. This study aims to analyze the factors influencing the compensation of AI professionals globally, with a particular focus on the
effects of company location, experience level, and required technical skills. Using a dataset of 15,000 AI job postings collected from multiple countries, a Multiple
Linear Regression (MLR) model was developed to identify predictive relationships between independent variables—location, experience, and skills—and the
dependent variable, annual salary in U.S. dollars. Data preprocessing included one-hot encoding for categorical variables, standardization of numerical attributes,
and vectorization of text-based skill descriptions. Model evaluation produced strong predictive results, with an R² of 0.82, a Mean Absolute Error (MAE) of 18,677
USD, and a Root Mean Squared Error (RMSE) of 25,704 USD. Statistical tests confirmed that company location and experience level significantly affected salary
outcomes (p < 0.05), while technical skills contributed only marginally. These findings suggest that structural factors such as geography and seniority play a more
decisive role in determining AI salaries than specific technical competencies. The study concludes that MLR offers a transparent and interpretable analytical
framework for exploring salary disparities in the global AI workforce. The results provide practical implications for organizations designing fair compensation
policies, professionals assessing market value, and educators aligning training programs with evolving industry demands
salary determinants in this sector. This study aims to analyze the factors influencing the compensation of AI professionals globally, with a particular focus on the
effects of company location, experience level, and required technical skills. Using a dataset of 15,000 AI job postings collected from multiple countries, a Multiple
Linear Regression (MLR) model was developed to identify predictive relationships between independent variables—location, experience, and skills—and the
dependent variable, annual salary in U.S. dollars. Data preprocessing included one-hot encoding for categorical variables, standardization of numerical attributes,
and vectorization of text-based skill descriptions. Model evaluation produced strong predictive results, with an R² of 0.82, a Mean Absolute Error (MAE) of 18,677
USD, and a Root Mean Squared Error (RMSE) of 25,704 USD. Statistical tests confirmed that company location and experience level significantly affected salary
outcomes (p < 0.05), while technical skills contributed only marginally. These findings suggest that structural factors such as geography and seniority play a more
decisive role in determining AI salaries than specific technical competencies. The study concludes that MLR offers a transparent and interpretable analytical
framework for exploring salary disparities in the global AI workforce. The results provide practical implications for organizations designing fair compensation
policies, professionals assessing market value, and educators aligning training programs with evolving industry demands
Creator
Siti Sarah Maidin1,*, Ding Yi2
, Yahya ‘Ayyasy3
, Yahya ‘Ayyasy3
Source
https://ijiis.org/index.php/IJIIS/article/view/213/144
Publisher
INTI International University, Nilai, Malaysia
Date
5 agustus 2024
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Siti Sarah Maidin1,*, Ding Yi2
, Yahya ‘Ayyasy3
, “A Multiple Linear Regression Approach to Predicting AI Professionals’
Salaries from Location and Skill Data,” Repository Horizon University Indonesia, accessed January 2, 2026, https://repository.horizon.ac.id/items/show/9712.
Salaries from Location and Skill Data,” Repository Horizon University Indonesia, accessed January 2, 2026, https://repository.horizon.ac.id/items/show/9712.