HR Analytics

Abstract

1. Introduction

The notebook's exploratory section asks four questions and answers them with plots. The rates below are the same relationships in numbers, computed over all 1470 rows, so you can check them against the CSV in one line of pandas.

Median monthly income is 3202 for leavers against 5204 for stayers, and attrition falls steadily with education, from 0.1824 below college to 0.1042 among the 48 employees at the highest level. Overtime is the strongest single signal in the exploration. And the fitted model agrees. The rigour notebook shuffles each column in turn and measures how much the model's ordering of people gets worse, and OverTime_Yes ranks second of the 59 features by that test. The exploration and the model agree, but not independently, because both are reading the same 1470 rows.

Everything in this section describes a dataset IBM invented. See the limitations before carrying any of it into a meeting.

2. Objective

This repository demonstrates how data science can help to identify the employee attrition which is part of Human Resource Management

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

4. Technology Stack

5. System Requirements

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

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• 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/netsatsawat/HR-Analytics.git
cd HR-Analytics

git clone https://github.com/netsatsawat/HR-Analytics.git
cd HR-Analytics
python -m venv .venv && source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r requirements.txt
jupyter notebook "code/HRM_Employee Attrition.ipynb"

brew install libomp     # xgboost, one of the ten models, cannot load without this
brew install graphviz   # only for the cell that draws the decision tree as a picture

python3 scripts/verify_readme_claims.py

ok  notebook image img/xgboost.png exists

every quoted README number matches its artifact

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 netsatsawat 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.