👥 HR Analytics — Employee Attrition Dashboard

🎯 Project Overview

A company is losing employees and wants to understand who is leaving, which departments are most affected, and what factors drive attrition. This project performs end-to-end HR data analysis using SQL Server and Power BI to identify attrition patterns and provide workforce retention recommendations.

Business Problem: 16.12% of employees are leaving — that's 1 in 6 people. Which departments and age groups are most at risk, and what can HR do about it?

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📁 Repository Structure

HR-Analytics-SQL-PowerBI/
│
├── sql/
│   └── hr_analysis.sql          # All SQL queries, views & KPI calculations
│
├── powerbi/
│   └── HR_Analytics_Dashboard.pbix  # Interactive Power BI dashboard
│
├── images/
│   └── dashboard.png            # Dashboard screenshot
│
└── README.md

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🛠️ Tools & Technologies

Tool	Purpose
SQL Server	Data storage, exploration, transformation & KPI calculation
SQL Views	Created reusable views connected directly to Power BI
Power BI	Interactive dashboard with filters and visuals
IBM HR Analytics Dataset	1,470 employee records with 35 attributes

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📦 Dataset

• Source: IBM HR Analytics Employee Attrition Dataset (Kaggle)
• Size: 1,470 employees | 35 features
• Key columns: Age, Department, JobRole, MonthlyIncome, Attrition, JobSatisfaction, YearsAtCompany

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🔄 Project Workflow

Step 1 → Imported IBM HR dataset into SQL Server
         ↓
Step 2 → Data Exploration (row counts, basic checks)
         ↓
Step 3 → Created derived columns in SQL
         (Age Groups, Attrition Flag 0/1)
         ↓
Step 4 → Built SQL Views for KPIs
         (vw_HR_Base, vw_HR_KPIs, vw_Attrition_By_Department, vw_AgeGroup_Attrition)
         ↓
Step 5 → Connected Power BI directly to SQL Server views
         ↓
Step 6 → Built interactive dashboard with 8 visuals + department filter

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🔍 Key Findings

Overall KPIs

Metric	Value
Total Employees	1,470
Employees Who Left	237
Attrition Rate	16.12%
Avg Monthly Income	$6,502

Finding 1 — Sales Department Has Highest Attrition

Department	Attrition Rate
Sales	20.63% ⚠️
Human Resources	19.05% ⚠️
Research & Development	13.84%

→ Sales loses 1 in 5 employees — the highest risk department

Finding 2 — Young Employees Leave the Most

Age Group	Total	Left
Under 25	97	38
25–34	554	112
35–44	505	51
45–54	245	25
55+	69	11

→ Employees under 34 account for the majority of attrition — early career retention is critical

Finding 3 — Low Job Satisfaction = High Attrition

Satisfaction Level	% Who Stayed
Level 4 (Highest)	88.67%
Level 3	83.48%
Level 2	83.57%
Level 1 (Lowest)	77.16% ⚠️

→ Employees with lowest job satisfaction churn at 22.84% — nearly double the company average

Finding 4 — Managers Earn 5x More Than Entry Level

Job Role	Avg Monthly Income
Manager	$17,200
Research Director	$16,000
Research Scientist	$3,200
Human Resources	$4,200

→ Large income gap between roles may contribute to dissatisfaction and attrition in lower-paid roles

Finding 5 — Gender Distribution

• Male: 882 employees (60%)
• Female: 588 employees (40%)

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💡 Business Recommendations

#	Recommendation	Target
1	Launch retention program for Sales department	Sales team (20.63% attrition)
2	Create career growth paths for employees under 34	Young workforce
3	Conduct job satisfaction surveys and act on Level 1 feedback	Low satisfaction employees
4	Review compensation structure for Research Scientists and HR roles	Lower-paid roles
5	Introduce mentorship programs to improve early-career retention	Under 25 age group

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📊 SQL Highlights

The project uses SQL Views connected directly to Power BI — the same approach used in real industry workflows:

-- KPI View used in Power BI dashboard
CREATE VIEW vw_HR_KPIs AS
SELECT 
    COUNT(*) AS Total_Employees,
    SUM(Attrition_Flag) AS Attrition_Count,
    CAST(SUM(Attrition_Flag) * 1.0 / COUNT(*) AS DECIMAL(6,4)) AS Attrition_Rate,
    CAST(AVG(MonthlyIncome) AS INT) AS Avg_Monthly_Income
FROM vw_HR_Base;

-- Age Group bucketing using CASE statement
CASE 
    WHEN Age < 25 THEN 'Under 25'
    WHEN Age < 35 THEN '25-34'
    WHEN Age < 45 THEN '35-44'
    WHEN Age < 55 THEN '45-54'
    ELSE '55+'
END AS Age_Group

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📈 Dashboard Preview

Dashboard Features

• Department filter (Human Resources / Research & Dev / Sales)
• 4 KPI cards — Total Employees, Attrition Count, Avg Income, Attrition Rate
• Employee by Gender donut chart
• Avg Monthly Income by Job Role bar chart
• Employee Distribution by Attrition donut chart
• Employees by Age Group and Attrition clustered bar chart
• Avg Monthly Income by Department and Attrition
• Attrition Rate by Department
• Attrition by Job Satisfaction Level stacked bar chart

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▶️ How to Run This Project

1. Clone this repository

git clone https://github.com/Devendra0602/HR-Analytics-SQL-PowerBI.git

2. Open SQL Server Management Studio (SSMS)

3. Run sql/hr_analysis.sql to create the database, table, and views

4. Import the IBM HR dataset into the HR_Employees table

5. Open powerbi/HR_Analytics_Dashboard.pbix in Power BI Desktop

6. Update the data source connection to your SQL Server instance

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👤 Author

Devendra Patil

• 📧 [patildevendra062@gmail.com]
• 💼 [https://www.linkedin.com/in/patil-devendra2701/]
• 🐙 github.com/Devendra0602

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📌 Resume Line for This Project

"Analyzed IBM HR dataset of 1,470 employees using SQL Server & Power BI — identified Sales department with 20.63% attrition rate and low job satisfaction employees churning at 22.84%, with 5 targeted retention recommendations"