💼 HR Employee Attrition Analysis using MySQL

This project is a comprehensive HR analytics case study using MySQL, based on IBM's HR dataset. The goal is to identify trends, patterns, and drivers of employee attrition, helping HR teams make data-driven decisions to improve retention.

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📂 Dataset

• Source: IBM HR Analytics Dataset (Kaggle)
• Format: CSV
• Columns Used (selected):
  • Age, Attrition, BusinessTravel, Department, DistanceFromHome
  • Education, EnvironmentSatisfaction, JobRole, MonthlyIncome
  • OverTime, PerformanceRating, TotalWorkingYears, YearsAtCompany

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🛠 Tools Used

Tool	Purpose
MySQL	Data storage and analysis
Excel	Preprocessing CSV (optional)
GitHub	Project documentation

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📈 Project Objectives

• Identify key factors contributing to employee attrition.
• Segment high-risk employee groups.
• Suggest actionable retention strategies using data.

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🔍 Key SQL Queries and Insights

✅ Attrition Rate by Department

💡 Insight: Departments like Sales or R&D have higher attrition — worth deeper investigation.

SELECT Department, COUNT() AS Total_Employees, SUM(CASE WHEN Attrition = 'Yes' THEN 1 ELSE 0 END) AS Attritions, ROUND(SUM(CASE WHEN Attrition = 'Yes') * 100.0 / COUNT(), 2) AS Attrition_Rate FROM hr_attrition GROUP BY Department;

✅ Income Group vs Attrition

💡 Insight: Lower income groups show significantly higher attrition.

SELECT CASE WHEN MonthlyIncome < 3000 THEN 'Low Income' WHEN MonthlyIncome BETWEEN 3000 AND 6000 THEN 'Mid Income' ELSE 'High Income' END AS Income_Bracket, COUNT() AS Total, SUM(CASE WHEN Attrition = 'Yes' THEN 1 ELSE 0 END) AS Attritions, ROUND(SUM(CASE WHEN Attrition = 'Yes') * 100.0 / COUNT(), 2) AS Attrition_Rate FROM hr_attrition GROUP BY Income_Bracket;

✅ High Risk Attrition Profile (Custom Scoring)

💡 Insight: Employees with low pay, long commute, and overtime are most at risk.

SELECT * FROM hr_attrition WHERE MonthlyIncome < 3000 AND DistanceFromHome > 10 AND OverTime = 'Yes';

✅ Job Role + Department Attrition Hotspots

💡 Insight: Focus on specific job-department pairs that drive attrition.

SELECT Department, JobRole, ROUND(SUM(CASE WHEN Attrition = 'Yes' THEN 1 ELSE 0 END)100.0 / COUNT(), 2) AS Attrition_Rate FROM hr_attrition GROUP BY Department, JobRole ORDER BY Attrition_Rate DESC LIMIT 5;

💡 More Queries:

• Overall Attrition Rate
• Attrition by Job Role
• Attrition by Age Group
• Impact of Overtime on Attrition
• Attrition by Distance from Home
• Monthly Income vs Attrition (Income brackets)
• Attrition by Department
• Job Role vs Monthly Income
• Attrition by Education Level
• Total Working Years vs Attrition
• Years at Company Grouped
• Performance Rating vs Attrition
• High Risk Employees (Low Income + High Distance + Overtime) #Advanced analyze
• Which combination of Job Role & Department shows highest attrition?
• Compare average tenure (YearsAtCompany) of employees who left vs stayed
• Attrition Risk Score (Custom Score Calculation)
• Employees with High Performance but Still Left
• Average Income vs Years of Experience (Working Years)
• Attrition by Distance Buckets
• Correlation between Environment Satisfaction & Attrition
• Cohort Analysis: Who joined <2 years ago and already left?
• Are young employees leaving more frequently?
• Employee Retention Ratio by Job Role

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📌 Key Insights

• OverTime is a strong attrition driver.
• Low-income and high-distance employees show higher turnover.
• New employees (0-2 years) leave more often — signaling onboarding/culture-fit issues.
• Some high-performing, well-paid employees still leave — possible job dissatisfaction or leadership gap.
• Helps HR target specific job roles within departments for retention strategies.
• Reveals whether long-serving employees or new joiners are leaving more.
• Use this to segment employees for proactive retention programs.
• Losing top performers is costly—this helps flag critical losses.
• Useful for HR compensation benchmarking.
• Helps HR with location-based working policy (WFH/flex commute).
• Poor workplace experience often correlates with employee churn.

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✅ Conclusion

Using simple yet powerful SQL analytics, this project uncovers valuable trends hidden in HR data. The goal is to help HR departments identify, understand, and act on attrition patterns — improving employee engagement and reducing talent loss.

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📁 Project Structure

📦 HR-Attrition-MySQL

┣ 📁 data

┃ ┗ hr_attrition.csv

┣ 📄 queries.sql

┣ 📄 insights.md

┣ 📄 README.md

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✍ Author

Aman Banothe

📎 LinkedIn : https://www.linkedin.com/in/aman-banothe-5174ba223/

🧠 Data Analyst | Power BI | MySQL | Tableau | Python

⭐ Want to go further? Build a Power BI or Tableau dashboard from this dataset and add a short case-study video or presentation to your portfolio.

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Would you like me to now generate:

• 📄 insights.md (for detailed query + insight documentation), or
• Start the next MySQL project: AdventureWorks Sales Analysis?

Just tell me: insights.md or Next Project.