Project-15-Python-For-Data-Analysis-HR

End-to-end HR Analytics on 2 million employee records | Analyzing workforce distribution, attrition, salary trends, performance & hiring patterns using Python, Pandas & Seaborn

👥 HR Data Analysis — Multinational Corporation (2M Employees)

An end-to-end HR Analytics project on 2,000,000 employee records, completed as part of the Python for Data Analysis course.

📌 Objective

Analyze workforce distribution, attrition rates, salary trends, performance ratings, hiring patterns, and Remote vs On-site comparisons for a global MNC.

📂 Dataset

• Source: HRData.csv
• Records: 2,000,000 employees
• Departments: IT, Marketing, HR, Operations, Finance, Sales, R&D
• Features: Employee_ID, Full_Name, Department, Job_Title, Hire_Date, Location, Performance_Rating, Experience_Years, Status, Work_Mode, Salary_INR

⚠️ Dataset not included due to file size (>25MB).

🛠️ Tools & Libraries

Library	Purpose
Pandas	Data cleaning, groupby, pivot_table, apply
Matplotlib	Bar charts, line charts, scatter plots
Seaborn	Heatmaps, boxplots

🔧 Key Pandas Techniques

Technique	Purpose
groupby().apply(nlargest)	Top-N records per group (Q14)
pivot_table()	Cross-tabulation: Dept × Job Title (Q6)
str.split().str[-1]	Extract country from Location string
resample() / dt.year	Time-based hire trend analysis
Attrition Rate formula	Resigned / Total per Dept × 100

❓ Analytical Questions (Q1 → Q15)

#	Question
Q1	Distribution of Employee Status
Q2	Distribution of Work Modes
Q3	Employee count per Department
Q4	Average salary by Department
Q5	Job title with highest average salary
Q6	Average salary by Department × Job Title (heatmap)
Q7	Resigned & Terminated count per Department
Q8	Salary variation with Years of Experience
Q9	Average performance rating by Department
Q10	Countries with highest employee concentration
Q11	Correlation between Performance Rating & Salary
Q12	Number of hires per year (trend)
Q13	Remote vs On-site salary comparison
Q14	Top 10 highest-paid employees per Department
Q15	Departments with highest attrition rate

📊 Analysis Structure

1. Imports & Setup
2. Load Dataset
3. First Look
4. Data Structure & Info
5. Data Cleaning & Feature Engineering
6. Analytical Questions (Q1 → Q15)
7. Key Insights

💡 Key Insights

• Active employees form the majority, but attrition is significant across departments
• R&D and Finance command the highest average salaries
• Salary grows with experience but plateaus after 12+ years
• Performance rating has weak positive correlation with salary
• Remote and On-site salaries are nearly identical — pay equity maintained
• Hiring accelerated significantly from 2018 onwards

🚀 How to Run

git clone https://github.com/ammarelsayed-2a/Project-15-Python-For-Data-Analysis-HR.git
cd Project-15-Python-For-Data-Analysis-HR
jupyter notebook "Project 15 HR.ipynb"

👤 Author

Ammar Elsayed — Python for Data Analysis | 2026
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