🧠 Employee Performance Predictor using Machine Learning

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📌 Project Overview

The Employee Performance Predictor is a Machine Learning-based HR analytics system that predicts employee performance levels:

• 🟢 High Performer
• 🟡 Medium Performer
• 🔴 Low Performer

It uses structured HR data such as experience, salary, attendance, training hours, and feedback scores to generate predictions.

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🎯 Business Problem

Organizations struggle with:

• Subjective employee evaluation
• Delayed performance feedback
• Inefficient training allocation
• Bias in promotions

This system solves it using data-driven HR decision-making.

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⚙️ Tech Stack

• Python
• Pandas, NumPy
• Scikit-learn
• Random Forest Classifier
• Joblib (Model Saving)

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🧠 Machine Learning Workflow

Data Generation → Feature Engineering → Model Training → Evaluation → Prediction → HR Recommendation

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📊 Features Used

• Age
• Experience
• Salary
• Training Hours
• Attendance Rate
• Projects Completed
• Feedback Score
• Department

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🚀 How It Works

1. Synthetic HR dataset is generated
2. Model is trained using Random Forest
3. Employee data is given as input
4. System predicts performance level
5. HR recommendation is generated

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💻 How to Run

pip install -r requirements.txt
python main.py

📸 Sample Output
👤 Employee: Rahul Sharma
🎯 Predicted Performance: High
📌 HR Recommendation: Promotion Recommended

🏗️ Project Structure
Employee-Performance-Predictor/
│
├── src/
│   ├── data_generator.py
│   ├── train_model.py
│   ├── predict.py
│
├── data/
├── models/
├── images/
├── main.py
└── README.md

📈 Business Impact
Improves HR decision accuracy
Reduces bias in performance reviews
Identifies training needs early
Helps in promotion planning

🔮 Future Enhancements
Streamlit Dashboard UI
SHAP Explainability
Real HR dataset integration
Cloud deployment (AWS/Render)
Email alert system for low performers

👨‍💻 Author
Muktai Vyawahare
Computer Science Engineering Student
AI/ML Developer