https://github.com/samiksha29-patil/hr-employee-data-analysis-visualization-in-python

This project focuses on analyzing an HR Employee Dataset that contains details about employees such as demographics, job status, salaries, performance reviews, satisfaction levels, and attrition reasons.
https://github.com/samiksha29-patil/hr-employee-data-analysis-visualization-in-python

csv-files data data-visualization dataanalysis matplotlib numpy pandas python seaborn

Last synced: 5 months ago
JSON representation

This project focuses on analyzing an HR Employee Dataset that contains details about employees such as demographics, job status, salaries, performance reviews, satisfaction levels, and attrition reasons.

• Host: GitHub
• URL: https://github.com/samiksha29-patil/hr-employee-data-analysis-visualization-in-python
• Owner: samiksha29-patil
• Created: 2025-09-05T18:14:43.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2025-09-07T12:03:31.000Z (5 months ago)
• Last Synced: 2025-09-07T14:19:47.978Z (5 months ago)
• Topics: csv-files, data, data-visualization, dataanalysis, matplotlib, numpy, pandas, python, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 1.18 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# 👨‍💼 HR Employee Data Analysis & Visualization in Python

## 📌 Overview

This project analyzes an **HR Employee Dataset** containing employee demographics, salaries, performance metrics, satisfaction scores, and attrition data.  

The main objective is to identify patterns and insights that can help HR teams improve **employee retention, engagement, and workforce diversity**.

---

## 🛠️ Technologies Used

- **Python**

- Libraries: `pandas`, `numpy`, `matplotlib`, `seaborn`

- Data: HR Employee Dataset (CSV/Excel)

---

## 📊 Key Areas of Analysis

- Demographics (gender, marital status, diversity representation)  

- Salary & Department Analysis  

- Performance & Engagement levels  

- Attrition & Termination reasons  

- Absences & Productivity impact  

---

## 📊 Visualizations

[![HR Visualization 1](https://github.com/samiksha29-patil/HR-Employee-Data-Analysis-Visualization-in-Python/blob/main/HR1.png)](https://github.com/samiksha29-patil/HR-Employee-Data-Analysis-Visualization-in-Python/blob/main/HR1.png)

[![HR Visualization 2](https://github.com/samiksha29-patil/HR-Employee-Data-Analysis-Visualization-in-Python/blob/main/HR2.png)](https://github.com/samiksha29-patil/HR-Employee-Data-Analysis-Visualization-in-Python/blob/main/HR2.png)

[![HR Visualization 3](https://github.com/samiksha29-patil/HR-Employee-Data-Analysis-Visualization-in-Python/blob/main/HR3.png)](https://github.com/samiksha29-patil/HR-Employee-Data-Analysis-Visualization-in-Python/blob/main/HR3.png)

[![HR Visualization 4](https://github.com/samiksha29-patil/HR-Employee-Data-Analysis-Visualization-in-Python/blob/main/HR4.png)](https://github.com/samiksha29-patil/HR-Employee-Data-Analysis-Visualization-in-Python/blob/main/HR4.png)

[![HR Visualization 5](https://github.com/samiksha29-patil/HR-Employee-Data-Analysis-Visualization-in-Python/blob/main/HR5.png)](https://github.com/samiksha29-patil/HR-Employee-Data-Analysis-Visualization-in-Python/blob/main/HR5.png)

---

## ✅ Key Insights

- **Production Department** has the highest employee count as well as turnover.  

- **Voluntary attrition** is more common than involuntary.  

- Employees with **higher engagement** show fewer absences.  

- **Salary gaps** exist across departments.  

- Satisfaction and performance scores are strongly linked.  

---

## 🚀 Conclusion

This analysis provides actionable insights for HR departments to:  

- Reduce turnover by addressing key attrition factors.  

- Align salaries across roles to improve fairness.  

- Improve engagement and satisfaction through targeted HR initiatives.  

---