📁 HR Employee Dataset — Exploratory Analysis

This project explores a dataset containing employee records from a U.S.-based company. The analysis addresses key human resources questions using Python (Pandas, Seaborn, Matplotlib) in a Jupyter Notebook environment.

📊 Objectives

The notebook focuses on answering the following analytical questions:

1. 🧑‍💼 Manager vs. Performance Score
   Are there differences in employee performance scores depending on their manager?

2. 🌱 Recruitment Source vs. Tenure
   Which hiring sources produce employees with the longest average tenure?

3. 💍 Marital Status vs. Satisfaction
   Is there a relationship between marital status and job satisfaction?

4. 🎂 Age Distribution
   What is the current age structure of employed staff?

5. 🧓 Age vs. Special Projects
   Do older employees participate in more special projects?

🛠️ Technologies Used

• Python 3
• Pandas
• Matplotlib
• Seaborn
• Jupyter Notebook

🗂️ Files

• HRDataset.csv — source data
• hr-employee-analysis.ipynb — main notebook with structured analysis
• README.md — this file

📌 Key Insights

• Certain managers have a clear pattern in their employees' performance distributions.
• Some recruitment sources (e.g., staffing agencies) are associated with higher employee retention.
• No strong correlation was observed between marital status and satisfaction.
• The age distribution suggests a mid-career-dominant workforce.
• Age does not directly predict the number of special projects assigned.