HR Analytics

Abstract

1. Introduction

HR leaders must align HR data and initiatives to the organization’s strategic goals. For example, a tech company may want to improve collaboration across departments to increase the number of innovative ideas built into their software. HR initiatives like shared workspaces, company events, collaborative tools, and employee challenges can be implemented to achieve this goal. To determine how successful initiatives are, HR analytics can be utilized to examine correlations between initiatives and strategic goals.

Through this data-driven approach, HR analytics can illuminate the major causes of attrition, and new policies, along with training programs, can be put in place to help mitigate the problem. For example, data might show that high-aspiration employees are not challenged or employees are frustrated with a certain management style. Human resources analysis will reveal these issues, and then it will be up to leadership to act.

Analyzing the HR Criteria of a Company and how they promote their Employees and keep Balance between them using Data Analytics, Data Visualizations, and Machine Learning Models for Classification Purposes.

2. Objective

Analyzing the HR Criteria of a Company and how they promote their Employees and keep Balance between them using Data Analytics, Data Visualizations, and Machine Learning Models for Classification Purposes.

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/sharmaroshan/HR-Analytics.git
cd HR-Analytics

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 sharmaroshan and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.