🧑‍💼 HR Data Analysis with Python – End-to-End Analytics Project

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📌 Overview

This project performs comprehensive HR data analysis using Python on a dataset of 2 million employee records.
It focuses on workforce distribution, hiring trends, performance analytics, salary insights, attrition patterns, and more.

The analysis transforms raw HR data into actionable insights for HR managers, analysts, and business leaders.

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🛠️ Tech Stack

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Jupyter Notebook

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📂 Dataset Details

The dataset contains 11 columns, including:

• Employee details
• Department & Job Title
• Hire Date & Country
• Salary
• Performance Rating
• Work Mode (Remote / On-site)
• Status (Active, Resigned, Retired, Terminated)

DATASET https://drive.google.com/drive/folders/1nuplJGha2kQlJDu7NPvBO5zN_8zotroH?usp=drive_link

⭐ Key Analysis Performed

1️⃣ Employee Status Distribution

Breakdown of:

• Active
• Resigned
• Terminated
• Retired

Pie-chart visualization included.

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2️⃣ Work Mode Distribution

Remote vs On-site workforce share.

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3️⃣ Employee Count by Department

Department-level analysis with bar charts.

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4️⃣ Average Salary by Department

Computed and visualized using Pandas groupby + Matplotlib.

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5️⃣ Highest Paying Job Titles

Top salaries by job roles — insights into compensation structures.

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6️⃣ Salary by Department & Job Title

Multi-level groupby analysis showing detailed salary comparisons.

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7️⃣ Resigned & Terminated Employees by Department

Attrition breakdown with bar charts.

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8️⃣ Salary vs Experience Years

Understanding how salary grows with experience.

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9️⃣ Performance Rating by Department

Department-wise average performance score.

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🔟 Countries with Highest Employee Concentration

Extracted from location → country parsing.

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1️⃣1️⃣ Correlation Analysis

Correlation between:

• Salary & performance rating
• Numeric features heatmap

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1️⃣2️⃣ Hiring Trend Analysis (Year-wise)

Shows annual hiring patterns over 16 years.

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1️⃣3️⃣ Salary Comparison: Remote vs On-site

Identifying if remote workers are paid more.

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1️⃣4️⃣ Top 10 Highest Paid Employees per Department

Department-wise salary ranking using nlargest().

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1️⃣5️⃣ Attrition Rate by Department

Resigned % per department (Resigned / Total * 100).
Sorted to find highest-risk departments.

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📊 Visualizations Included

• Pie charts
• Countplots
• Bar charts
• Heatmaps
• Yearly hiring trend graph
• Department vs Job salary chart

All visuals generated using Matplotlib and Seaborn.

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📁 Project Structure

📦 HR-Data-Analysis
 ┣ 📜 HR_Data_Analysis.ipynb
 ┣ 📜 HR_Data.csv
 ┣ 📜 README.md
 ┗ 📂 images/ (optional charts)

📌 Author

👤 Loganathan
loganathanvizasia@gmail.com

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⭐ If you like this project, don’t forget to star the repository!