📊 HR Data Analysis with Python 📌 Project Overview

This project focuses on analyzing an HR dataset using Python to uncover insights related to employee distribution, salary trends, work modes, attrition patterns, and hiring trends. The objective is to support HR and management teams in making data-driven decisions for workforce planning, employee retention, and compensation strategies.

🎯 Objectives

Analyze employee status (Active, Resigned, Retired)

Understand department-wise employee distribution

Compare salary trends across departments and job roles

Analyze remote vs onsite work patterns

Study employee attrition trends

Examine hiring trends over time

🛠️ Tools & Technologies

Python

Pandas – data manipulation

NumPy – numerical operations

Matplotlib & Seaborn – data visualization

Jupyter Notebook

📂 Dataset Description

The dataset contains anonymized HR-related information, including:

Employee status

Department and job role

Salary

Work mode (Remote / Onsite)

Hire date

Location details

(Dataset used for educational and analytical purposes.)

📊 Analysis Performed

Employee status distribution analysis

Department-wise employee count

Average salary by department

Remote vs onsite salary comparison

Attrition rate analysis by department

Hiring trend analysis over the years

Correlation analysis using heatmaps

🔍 Key Insights

The majority of employees are actively employed, indicating workforce stability.

Salary levels vary significantly across departments, reflecting differences in role complexity and seniority.

Remote employees show salary trends comparable to onsite employees.

Certain departments experience higher attrition, suggesting potential engagement or workload issues.

Hiring trends reveal growth during specific years, indicating organizational expansion phases.

💡 Business Recommendations

Focus retention strategies on departments with higher attrition rates.

Periodically review compensation structures to remain competitive.

Continue or expand flexible and remote work policies.

Use historical hiring trends for better workforce planning.

Incorporate employee satisfaction and performance data for deeper insights.

✅ Conclusion

This project demonstrates an end-to-end HR data analysis workflow using Python—from data exploration and cleaning to visualization and insight generation. The findings can help organizations improve employee retention, optimize salary structures, and enhance workforce planning.

🚀 Future Enhancements

Build predictive models to forecast employee attrition

Integrate employee performance and satisfaction data

Develop interactive dashboards using Power BI or Tableau

Automate HR reporting processes

📁 Project Files

HR Data Analysis with Python.ipynb – Jupyter Notebook

HR Data Analysis with Python.pdf – Project report (PDF)

README.md – Project documentation

👤 Author Aditya Mankar