From b6bccf4d97ce6d61f0e1674c5808ee66c3504b76 Mon Sep 17 00:00:00 2001
From: VADAPALLI RAGHUPATHI <2400030362@kluniversity.in>
Date: Wed, 26 Nov 2025 08:30:35 +0530
Subject: [PATCH] feat: Add Employee Attrition Prediction ML project

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+# Employee Attrition Prediction
+
+## Description
+A machine learning model to predict employee attrition (turnover) in organizations. This project helps HR departments identify employees who are likely to leave the company, enabling proactive retention strategies.
+
+## Project Structure
+```
+Employee-Attrition-Prediction/
+├── data/                    # Dataset files
+├── notebooks/               # Jupyter notebooks
+├── src/                     # Source code
+├── models/                  # Saved models
+├── requirements.txt         # Dependencies
+└── README.md               # Project documentation
+```
+
+## Dataset
+The dataset includes employee information such as:
+- Demographics (age, gender, marital status, education)
+- Job-related factors (department, job role, years at company)
+- Compensation (salary, stock options, overtime)
+- Work-life balance metrics
+- Performance ratings and satisfaction scores
+
+## Installation
+```bash
+pip install -r requirements.txt
+```
+
+## Usage
+```python
+from src.model import AttritionPredictor
+
+predictor = AttritionPredictor()
+predictor.load_model('models/attrition_model.pkl')
+prediction = predictor.predict(employee_data)
+```
+
+## Model Details
+- **Algorithm**: Random Forest, Gradient Boosting, Neural Network
+- **Features**: 20+ engineered features including tenure, satisfaction index
+- **Metrics**: Accuracy, Precision, Recall, F1-Score, AUC-ROC
+
+## Results
+| Model | Accuracy | Precision | Recall | F1-Score |
+|-------|----------|-----------|--------|----------|
+| Random Forest | 0.88 | 0.85 | 0.82 | 0.83 |
+| Gradient Boosting | 0.89 | 0.86 | 0.84 | 0.85 |
+| Neural Network | 0.87 | 0.83 | 0.80 | 0.81 |
+
+## Key Insights
+- Overtime and work-life balance are top predictors of attrition
+- Job satisfaction significantly impacts retention
+- Employees with fewer years at company have higher attrition risk
+
+## Contributing
+Contributions are welcome! Please read the contributing guidelines before submitting a pull request.
+
+## License
+MIT License