👥 HR Employee Attrition Analysis

Tools: Python · Pandas · Matplotlib · Seaborn · Power BI
Dataset: IBM HR Analytics Dataset (Kaggle) — 1,470 employees
Status: ✅ Complete

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🎯 Business Problem

Why are employees leaving the company? This analysis identifies the key drivers of attrition and provides data-backed retention recommendations for the HR team.

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🛠 Tools & Process

1. Data Cleaning — removed irrelevant columns, handled missing values
2. EDA — Python (Pandas, Matplotlib, Seaborn)
3. Dashboard — Power BI interactive dashboard

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📈 Key Findings

• Overall attrition rate is 16.1% — 237 out of 1,470 employees left
• Sales department has the highest attrition rate
• Employees working OverTime are 3x more likely to leave
• Age group 18-25 has the highest attrition rate
• Employees who left earned $2,000 less per month than those who stayed

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💡 Business Recommendations

1. Reduce overtime — strongest predictor of attrition
2. Focus retention on Sales dept — highest risk department
3. Review salary bands for junior employees — income gap is significant

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📂 Files

File	Description
notebooks/analysis.ipynb	Full Python analysis
outputs/chart1_department.png	Attrition by department
outputs/chart2_overtime.png	Overtime impact
outputs/chart3_age_group.png	Attrition by age group
outputs/chart4_job_satisfaction.png	Job satisfaction analysis
outputs/chart5_income.png	Income distribution
outputs/dashboard_screenshot.png	Power BI dashboard

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📸 Dashboard Preview