Exit Narrative Generator

An explanation-first HR analytics system that reconstructs why employee exit becomes rational. Instead of predicting attrition, it generates human-readable exit narratives by decomposing pressure and retention forces, adding peer context and counterfactual interventions to reveal how stability erodes over time.

Data SciencePythonMIT

Abstract

Exit Narrative Generator is an open-source Data Science project. An explanation-first HR analytics system that reconstructs why employee exit becomes rational. Instead of predicting attrition, it generates human-readable exit narratives by decomposing pressure and retention forces, adding peer context and counterfactual interventions to reveal how stability erodes over time. The Exit Narrative Generator is a systems-level interpretability engine that transforms employee attrition data into coherent, human-readable narratives explaining why exit pressure accumulates, how stabilizing forces erode, and which small interventions could have plausibly changed the outcome. It is built using Python. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building a Data Science mini project or final-year project.

1. Introduction

The Exit Narrative Generator is a systems-level interpretability engine that transforms employee attrition data into coherent, human-readable narratives explaining why exit pressure accumulates, how stabilizing forces erode, and which small interventions could have plausibly changed the outcome.

This project is not a prediction engine. It is a post-hoc explanatory system designed to surface meaning, not alerts.

2. Objective

An explanation-first HR analytics system that reconstructs why employee exit becomes rational. Instead of predicting attrition, it generates human-readable exit narratives by decomposing pressure and retention forces, adding peer context and counterfactual interventions to reveal how stability erodes over time.

This project demonstrates how Python can be applied to a real-world Data Science problem.

4. Technology Stack

Python

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/AmirhosseinHonardoust/Exit-Narrative-Generator.git
cd Exit-Narrative-Generator

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Turn the analysis into an interactive dashboard
  • Automate data refresh with a scheduled job
  • Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What is the source of the dataset and how was missing data handled?
  2. Which exploratory analysis steps revealed the most useful insight?
  3. Why were these particular charts chosen to present the data?
  4. Which statistical or ML technique supports the conclusions?
  5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by AmirhosseinHonardoust and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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