🧠 Employee Insight AI

Intelligent Predictive Analytics for Workforce Decisions

A multi-output ML model that predicts employee attrition risk, performance, satisfaction level, and rating — enabling HR teams to make data-driven decisions.

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🌟 Overview

Employee Insight AI is a next-generation HR analytics solution built using machine learning.
It processes employee data to predict four major outcomes:

• 🧳 Attrition Risk → Probability of employee leaving
• 📈 Performance Score → Predicts if employee meets or exceeds expectations
• 😊 Satisfaction Level → Estimates morale and engagement
• ⭐ Current Rating → Predicts numerical performance rating (1–5 scale)

This project is ideal for HR analysts, data scientists, and organizations seeking smarter workforce analytics.

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📊 Example Predictive Results

Full Predictive Analytics Results Attrition Risk: High (Probability: 53.2%)

Performance Score: Fully Meets (Encoded Value: 3)

Satisfaction Level: High (Encoded Value: 4)

Current Rating: 2.97 (Rounded: 3)

yaml Copy code

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🧠 Model Workflow

flowchart LR A[Employee Data] --> B[Data Preprocessing] B --> C[Feature Encoding & Scaling] C --> D[Machine Learning Model] D --> E[Multi-output Predictions] E --> F[Visualization / Dashboard] ⚙️ Tech Stack Category Tools & Technologies Language Python 🐍 Libraries Pandas, NumPy, Scikit-learn, Matplotlib, Pickle Framework Flask / Streamlit Model Type Multi-Output Classification & Regression Dataset Employee Analytics Data (CSV / Excel)

🚀 Installation & Setup 🔹 1. Clone the Repository bash Copy code git clone https://github.com/your-username/employee-insight-ai.git cd employee-insight-ai 🔹 2. Install Dependencies bash Copy code pip install -r requirements.txt 🔹 3. Run the App For Streamlit Interface:

bash Copy code streamlit run app.py Or Flask Backend:

bash Copy code python app.py 📁 Folder Structure text Copy code employee-insight-ai/ ├── app.py ├── model/ │ ├── trained_model.pkl │ ├── scaler.pkl │ ├── model_columns.pkl ├── data/ │ └── employee_data.csv ├── static/ ├── templates/ │ └── index.html ├── requirements.txt └── README.md

🧩 Feature Details (click to expand)

Feature Description Attrition Risk Prediction Predicts how likely an employee is to leave the company Performance Score Estimates performance level (Below, Meets, Exceeds) Satisfaction Level Measures employee satisfaction on qualitative scale Current Rating Predicts rating between 1–5 based on multiple metrics

🧪 Model Pipeline Data Preprocessing – Cleans and encodes categorical features

Feature Scaling – Normalizes numerical data

Model Training – Uses classification/regression algorithms

Prediction – Outputs encoded & readable results

Visualization – Displays data insights and probabilities

🌐 Use Cases 🧩 HR Teams: Predict and reduce attrition before it happens

📈 Managers: Evaluate team satisfaction and performance trends

🧮 Data Analysts: Build workforce dashboards and analytics pipelines

🔮 Future Enhancements

🔁 Continuous model retraining pipeline

🤖 AI-powered recommendations for employee retention

👨‍💻 Developer Anshul Sharma 🎓 B.Tech Data Science | 💼 Data Scientist & Software Engineer 📍 India

🪪 License This project is licensed under the MIT License – you're free to use, modify, and distribute it.

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