{
  "abstract": "This manuscript describes an explainable employee attrition prediction and risk-scoring application developed using Python, Logistic Regression, SHAP explanations, and Streamlit. The application provides a dashboard, employee-level risk profiles, department analysis, and what-if simulations. It is intended as an educational decision-support prototype, not as an automated employment decision system. Formal evaluation metrics and further validation are required before making claims about predictive performance or using the system operationally.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Aditya"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7aq5gqtscy0d0.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23441088",
  "fulltext_url": null,
  "id": "7aq5gqtscy0d0",
  "issns": [],
  "landing_url": "https://zenodo.org/records/23441088",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://zenodo.org/api/records/23441088/files/Employee%20Attrition%20Prediction%20and%20Risk%20Scoring%20System%20%E2%80%93%20Research%20Paper.pdf/content",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-17",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7aq5gqtscy0d0.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:21:09Z",
      "native_id": "23441088",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23441088"
    }
  ],
  "subjects": [],
  "title": "Employee Attrition Prediction and Risk Scoring System: An Explainable Machine-Learning Application",
  "title_text": "Employee Attrition Prediction and Risk Scoring System: An Explainable Machine-Learning Application",
  "type": "preprint",
  "updated_at": "2026-10-31T12:21:09Z",
  "url": "https://research.draconitic.com/paper/7aq5gqtscy0d0",
  "venue": "Zenodo",
  "versions": []
}