📊 HR Workforce Analysis Dashboard

Built with Microsoft Power BI | IBM HR Analytics Dataset

---

📌 Project Overview

This end-to-end Power BI dashboard analyzes workforce data from IBM's HR Analytics dataset (1,470 employees) to help HR managers make data-driven decisions around attrition, demographics, compensation, and department performance.

---

❓ Business Problem

Organizations struggle to identify why employees leave and which groups are most at risk. Without clear visibility into workforce patterns, HR teams cannot build effective retention strategies.

This dashboard answers:

• What is our current attrition rate and who is leaving?
• Which departments and job roles have the highest turnover?
• How do demographics (age, gender, education) impact attrition?
• How does compensation compare across departments and roles?

---

🛠️ Tools Used

Tool	Purpose
Power BI Desktop	Dashboard development
Power Query	Data cleaning & transformation
DAX	Calculated measures & KPIs
Excel/CSV	Data source

---

📊 Dashboard Pages

1️⃣ Overview

High-level summary of workforce KPIs including total employees, attrition count, attrition rate, and age distribution.

2️⃣ Attrition Analysis

Deep dive into attrition patterns by job role, education field, years at company, job satisfaction and marital status.

3️⃣ Employee Demographics

Workforce composition analysis covering age groups, gender distribution, education levels, and salary distribution across job roles.

4️⃣ Department View

Side-by-side department comparison of headcount, attrition rate, average income, job satisfaction and employee tenure.

---

🔍 Key Insights

• 📌 Overall attrition rate is 16.12% — above healthy industry benchmark of 10%
• 📌 Sales department has highest attrition rate at 20.6% despite competitive salaries
• 📌 Year 1 shows the highest attrition spike — early retention programs are critical
• 📌 Single employees leave significantly more than married employees
• 📌 Life Sciences background has the highest attrition among education fields
• 📌 Managers earn nearly 3x more than entry-level roles — large compensation gap exists
• 📌 R&D department makes up 65% of total workforce

---

📐 DAX Measures

-- Total Employees
Total Employees = 
COUNTROWS('HR Analytics Data')

-- Attrition Count
Attrition Count = 
CALCULATE(
    COUNTROWS('HR Analytics Data'),
    'HR Analytics Data'[Attrition] = "Yes"
)

-- Attrition Rate
Attrition Rate = 
DIVIDE([Attrition Count], [Total Employees], 0)

-- Active Employees
Active Employees = 
CALCULATE(
    COUNTROWS('HR Analytics Data'),
    'HR Analytics Data'[Attrition] = "No"
)

-- Average Monthly Income
Avg Monthly Income = 
AVERAGE('HR Analytics Data'[MonthlyIncome])

-- Average Age
Average Age = 
AVERAGE('HR Analytics Data'[Age])

-- Average Tenure
Avg Tenure = 
AVERAGE('HR Analytics Data'[YearsAtCompany])

-- Male Employees
Male Employees = 
CALCULATE(
    [Total Employees],
    'HR Analytics Data'[Gender] = "Male"
)

-- Female Employees
Female Employees = 
CALCULATE(
    [Total Employees],
    'HR Analytics Data'[Gender] = "Female"
)

-- High Salary Employees
High Salary Employees = 
CALCULATE(
    [Total Employees],
    'HR Analytics Data'[MonthlyIncome] > 10000
)

---

📁 Data Source

• Dataset: IBM HR Analytics Employee Attrition & Performance
• Source: Kaggle
• Size: 1,470 rows × 35 columns
• License: Open Database License (ODbL)

---

🚀 How to Use

1. Clone or download this repository
2. Open HR_Workforce_Analysis.pbix in Power BI Desktop
3. Data is already loaded — no setup required
4. Use slicers to filter by Department, Gender, Education
5. Navigate between pages using the top navigation bar

---

📸 Dashboard Preview

Overview	Attrition Analysis

Demographics	Department View

---

🤝 Connect With Me

Imran Khan 🎓 Digital Technology & Management Student — OTH Amberg-Weiden

---

⭐ If you found this project helpful, please give it a star!