employee-performance-classifier

jomarie04/employee-performance-classifier

employee-performance-classifier is a machine learning model from jomarie04. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.

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

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Updated Jan 27, 2026

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License: mit
Access: Public
Created: Jan 27, 2026
Updated: Jan 27, 2026
SHA: f49bc9db

Model card

From the Hugging Face model README

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.

Intended Use

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

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)

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)

Training Procedure

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

Evaluation Results

Metric	Score
Accuracy	0.86
F1-score	0.84

(Results may vary depending on random seed)

Limitations

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

Ethical Considerations

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