jomarie04/employee-performance-classifier

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language: en license: mit tags: - tabular-data - classification - synthetic-data - machine-learning datasets: - custom metrics: - accuracy - f1

Employee Performance Classification Model

Model Description

This model is a machine learning classifier trained on a synthetic employee performance dataset.
It predicts employee performance ratings based on demographic, education, and job-related features.

The model is intended for educational, demonstration, and prototyping purposes only.

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Intended Use

• ✅ ML demos and tutorials
• ✅ Prototyping HR analytics systems
• ✅ Hugging Face Spaces demos
• ❌ Not for real-world HR decision-making

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Model Details

• Model type: Tabular classification
• Algorithm: Random Forest / XGBoost / Neural Network (example)
• Framework: scikit-learn / PyTorch
• Input: Structured CSV data
• Output: Performance rating (1–5)

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Training Data

The dataset is synthetically generated and contains the following fields:

Feature	Type	Description
age	Integer	Employee age
gender	Categorical	Gender
department	Categorical	Department name
years_experience	Integer	Years of experience
education_level	Categorical	Highest education
monthly_salary	Float	Monthly salary
performance_rating	Integer	Target label (1–5)

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Training Procedure

• Train/Validation Split: 80/20
• Evaluation Metrics: Accuracy, F1-score
• Preprocessing:
  • One-hot encoding for categorical features
  • Feature scaling for numerical values

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Evaluation Results

Metric	Score
Accuracy	0.86
F1-score	0.84

(Results may vary depending on random seed)

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Limitations

• Data is synthetic and may not reflect real-world bias
• Model should not be used for real employee evaluations
• Limited feature diversity

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Ethical Considerations

This model avoids using real personal data.
However, performance prediction systems can introduce bias if misused.

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License

MIT License

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Citation

If you use this model, please cite: