This project involves a predictive model to determine whether employees are likely to leave or stay within the company.
The objective is to assist HR departments in identifying employees at risk of leaving and implementing retention strategies.
Model success will be measured by accuracy, precision, recall, and F1 score for predicting employee turnover.
- Access to employee data including job history, performance, and other relevant features.
- Python environment with necessary libraries.
- Data preprocessing scripts.
- Performance, accuracy, and maintainable code.
- Ethical handling of employee data.
- Limited to structured employee data.
- Model's predictions depend on input data quality.
- Real-time predictions.
- External factors not explicitly included.
Predict employee attrition as a binary classification task using historical employee data.
Employee records with attributes like tenure, performance ratings, promotions, salary, etc.
Apply classification algorithms such as logistic regression, decision trees, and neural networks. Feature engineering and selection will be employed.
Data collection -> Data preprocessing -> Feature engineering -> Model training -> Evaluation.
- Data Collection: Gather employee data.
- Data Preprocessing: Clean, handle missing values.
- Feature Engineering: Create relevant features.
- Model Training: Train classification models.
- Evaluation: Measure model performance.
Predictive model aids in employee retention efforts. Model's efficacy depends on data quality and appropriate techniques. Potential for real-time deployment and broader external factor analysis in future.