Dataset for predictive modelling and analysis of employee attrition and retention

This dataset provides insights into the factors affecting employee attrition in the private sector of Saudi Arabia. The data were collected through an online survey of 1191 participants working in Saudi Arabia's private sector companies and institutions across various industries. The questionnaire consists of three categories of variables: demographic information, job-related features, and psychological and satisfaction variables. For each category in the questionnaire, several questions were disseminated to a sample population. Preprocessing and analysis were conducted to identify the principal factors influencing employee attrition and job satisfaction. The dataset was formulated with 34 attributes and 1191 rows. In addition, the dataset includes nominal and ordinal answers for the variables, allowing for sophisticated analyses using machine learning classification techniques. The dataset also contains the independent variable (target variable), about employee attrition information, which acts as a class or label variable in machine learning scenarios. We intend for this dataset to serve as a valuable resource for researchers and organizations aiming to understand and predict employee turnover, as it provides a set of attributes that can help formulate workforce management strategies and policies.

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Publication Details

Journal
Data in Brief
Published
2025-11-06
DOI
https://doi.org/10.1016/j.dib.2025.112242
Primary Topic
AI and HR Technologies
Type
article
Field-Weighted Citation Impact
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article

Dataset for predictive modelling and analysis of employee attrition and retention

Haya Alqahtani, Hana Almagrabi, Amal Alharbi
Data in Brief
AI and HR Technologies
article

Dataset for predictive modelling and analysis of employee attrition and retention

Haya Alqahtani, Hana Almagrabi, Amal Alharbi
article en

Abstract

This dataset provides insights into the factors affecting employee attrition in the private sector of Saudi Arabia. The data were collected through an online survey of 1191 participants working in Saudi Arabia's private sector companies and institutions across various industries. The questionnaire consists of three categories of variables: demographic information, job-related features, and psychological and satisfaction variables. For each category in the questionnaire, several questions were disseminated to a sample population. Preprocessing and analysis were conducted to identify the principal factors influencing employee attrition and job satisfaction. The dataset was formulated with 34 attributes and 1191 rows. In addition, the dataset includes nominal and ordinal answers for the variables, allowing for sophisticated analyses using machine learning classification techniques. The dataset also contains the independent variable (target variable), about employee attrition information, which acts as a class or label variable in machine learning scenarios. We intend for this dataset to serve as a valuable resource for researchers and organizations aiming to understand and predict employee turnover, as it provides a set of attributes that can help formulate workforce management strategies and policies.

Data in BriefVol. 63
King Abdulaziz University (SA)
Openalex Percentile: Top 62%
AI and HR Technologies
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Dataset for predictive modelling and analysis of employee attrition and retention — Haya Alqahtani, Hana Almagrabi, et al. · Data in Brief (2025) | TGRS Research Map | TGRS