pavansubhasht/ibm-hr-analytics-attrition-dataset: papers linking to it

Kaggle · www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to pavansubhasht/ibm-hr-analytics-attrition-dataset (https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

4 papers in the tracked journals link to the Kaggle dataset pavansubhasht/ibm-hr-analytics-attrition-dataset in their Crossref records, the first in 2023; most of them (2) in 2026.
By category: AI (3) and Economics (1); the journals with the most are Computational Economics (1), Expert Systems with Applications (1) and Neural Computing and Applications (1).

By year#

20232026

Papers linking to pavansubhasht/ibm-hr-analytics-attrition-dataset by publication year
Year	Papers
2023	1
2025	1
2026	2

Journals#

Journals whose papers link to pavansubhasht/ibm-hr-analytics-attrition-dataset most
Journal	Papers
Computational Economics	1
Expert Systems with Applications	1
Neural Computing and Applications	1
Knowledge and Information Systems	1

Papers linking to pavansubhasht/ibm-hr-analytics-attrition-dataset, most cited first
Published	Title	Journal	Authors	Citations	Link
2023-10-28	A modified reverse-based analysis logic mining model with Weighted Random 2 Satisfiability logic in Discrete Hopfield Neural Network and multi-objective training of Modified Niched Genetic Algorithm	Expert Systems with Applications	Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin et al.	71	cites it
2025-07-18	An in-depth analysis of KernelSHAP and SamplingSHAP: assessing robustness, error, and efficiency	Knowledge and Information Systems	Beyza Aydoğan, Tevfik Aytekin	3	cites it
2026-06-01	Class imbalanced safe semi-supervised learning for Customer Churn Prediction (CISL)	Neural Computing and Applications	Salma Abdelmonem, Dina Elreedy, Samir I. Shaheen	0	cites it
2026-06-06	Leveraging Explainable Machine Learning for Employee Attrition Prediction: Practical Implications and Job Embeddedness Insights	Computational Economics	Dara Thomas, Ying Li, Chiagoziem C. Ukwuoma et al.	0	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). pavansubhasht/ibm-hr-analytics-attrition-dataset: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-pavansubhasht-ibm-hr-analytics-attrition-dataset

@misc{scholar4ai-code-kaggle-pavansubhasht-ibm-hr-analytics-attrition-dataset,
  author = {{Scholar4AI}},
  title = {pavansubhasht/ibm-hr-analytics-attrition-dataset: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-pavansubhasht-ibm-hr-analytics-attrition-dataset}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).