IBM HR Analytics Employee Attrition And Performance Prediction

Abstract

1. Introduction

IBM-HR-Analytics-Employee-Attrition-and-Performance-Prediction

The project stems from the potential to improve employee satisfaction, reduce costs, enhance organizational performance, and create a positive workplace culture. It's an opportunity to use data and analytics to make meaningful changes that benefit both employees and the organization as a whole.

HR analytics is the process of collecting and analyzing Human Resource (HR) data in order to improve an organization’s workforce performance. The process can also be referred to as talent analytics, people analytics, or even workforce analytics. This method of data analysis takes data that is routinely collected by HR and correlates it to HR and organizational objectives. Doing so provides measured evidence of how HR initiatives are contributing to the organization’s goals and strategies.

2. Objective

In this project, we enlisted the numerical and categorical attributes present in the publicly available dataset. Missing values were dropped to give better insights in data analysis. ANOVA and Chi-Square tests were carried out during statistical analysis. Machine Learning algo's were applied to understand, manage, and mitigate employee attrition.

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

4. Technology Stack

• Matplotlib
• LightGBM
• CatBoost
• Warnings

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
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/shantanu1109/IBM-HR-Analytics-Employee-Attrition-and-Performance-Prediction.git
cd IBM-HR-Analytics-Employee-Attrition-and-Performance-Prediction

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 shantanu1109 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.