Exploring Employee Resignations in the Queensland Education Sector

This project analyzes resignation data from the Queensland Department of Education to explore why employees resign and whether resignation reasons differ across demographic groups.
It focuses on understanding patterns related to job satisfaction, tenure, and age.

📊 Objectives

• Analyze reasons for employee resignations across service years and age groups.
• Determine whether shorter-tenure employees resign more due to dissatisfaction.
• Identify demographic trends to support HR strategy and retention.

🧰 Tools and Libraries

• Python
• pandas
• NumPy
• Matplotlib
• Jupyter Notebook

🧩 Key Steps

1. Load and inspect the employee resignation datasets.
2. Clean and merge datasets (standardize column names, handle missing values).
3. Categorize resignations (dissatisfaction-related vs. others).
4. Group by age and years of service to identify patterns.
5. Visualize resignation trends by age group and tenure.

💡 Insights

• Employees with fewer than 3 years of service show higher resignation rates due to dissatisfaction.
• Younger employees tend to resign more often for career progression or dissatisfaction.
• Mid-career employees (aged 30–45) show a more balanced distribution of resignation reasons.

🏁 About

This project was inspired by a guided project from DataQuest.io.
All analysis, code, and conclusions were independently developed.