💼 Employee Performance Predictor using Data Analytics

An AI-powered web application that predicts employee performance (High / Medium / Low) using Machine Learning and visualizes results through an interactive dashboard.

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📌 Project Overview

This project helps HR teams and managers:

• Predict employee performance
• Identify high and low performers
• Support promotion and training decisions
• Improve workforce productivity using data-driven insights

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🎯 Problem Statement

Organizations often rely on manual evaluation methods, which can be biased and inefficient.

This system uses Machine Learning to:

• Analyze employee attributes
• Predict performance levels
• Provide actionable HR insights

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⚙️ Tech Stack

• Python
• Pandas
• NumPy
• Scikit-learn
• Streamlit
• Plotly

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🏗️ Project Structure

Employee-Performance-Predictor/
│
├── app/
│   └── app.py
│
├── data/
│   └── employee_data.csv
│
├── images/
│   ├── dashboard.png
│   ├── prediction_high.png
│   ├── prediction_medium.png
│   ├── prediction_low.png
│
├── outputs/
│   └── predictions.csv
│
├── requirements.txt
└── README.md

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🚀 Features

• 📊 Interactive dashboard
• 🎯 Real-time performance prediction
• 📈 Semi-circle gauge visualization
• 💡 HR recommendations
• 🎨 Clean and professional UI

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🧠 Machine Learning Model

• Model Used: Random Forest Classifier

Input Features:

• Age
• Experience
• Department
• Salary
• Training Hours
• Projects
• Feedback Score

Output:

• Low
• Medium
• High

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▶️ How to Run the Project

1️⃣ Clone Repository

git clone https://github.com/VaishnavaDevi-R/Employee-Performance-Predictor.git
cd Employee-Performance-Predictor

2️⃣ Create Virtual Environment

python -m venv venv
venv\Scripts\activate

3️⃣ Install Requirements

pip install -r requirements.txt

4️⃣ Run Application

python -m streamlit run app/app.py

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📊 Output Screenshots

🖥️ Dashboard

📊 confusion Matrix

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🚀 Future Importance

📈 Results

• Accurate classification of employee performance
• Visual insights using gauge charts
• Easy-to-use dashboard for HR teams

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💡 Key Insights

• Higher feedback score and more projects lead to high performance
• Low training hours may reduce performance
• Experience plays an important role in prediction

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🚀 Future Improvements

• Add real-world HR dataset
• Deploy application online
• Add feature importance visualization
• Integrate employee attrition prediction

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👩‍💻 Author

Vaishnava Devi

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📢 Conclusion

This project demonstrates how Data Analytics and Machine Learning can improve HR decision-making and help build intelligent workforce systems.