Employee Churn Predictor Excel Add-in

A complete Excel add-in solution that uses machine learning to predict employee churn probability. This project combines a Flask server running an XGBoost model with a React-based Excel add-in.

🎯 Features

• Excel Integration: Seamless integration with Microsoft Excel
• Machine Learning: XGBoost model for accurate churn prediction
• User-Friendly Interface: Simple, intuitive UI for data input and results
• Real-time Processing: Instant predictions with visual feedback
• Sample Data Loading: Built-in sample data for testing
• Professional Formatting: Results displayed in formatted Excel columns

📸 Screenshots

Sample Data Loading

Excel worksheet populated with sample employee data ready for prediction

Prediction Results

Churn predictions displayed in a new column with color-coded formatting

🏗️ Architecture

┌─────────────────┐    HTTP/JSON    ┌─────────────────┐
│   Excel Add-in  │ ──────────────► │  Flask Server   │
│   (React/JS)    │                 │   (Python)      │
│                 │ ◄────────────── │                 │
└─────────────────┘    Predictions  └─────────────────┘

📁 Project Structure

Excel_web_app/
├── flask_server/                 # Python Flask backend
│   ├── app.py                   # Main Flask application
│   ├── XGBoost.pkl             # Trained ML model
│   ├── requirements.txt         # Python dependencies
│   └── test_server.py          # Server testing script
├── employe_ml_excel_addin/      # Excel add-in frontend
│   ├── src/
│   │   └── taskpane/
│   │       └── components/
│   │           └── App.jsx      # Main React component
│   ├── manifest.xml             # Excel add-in configuration
│   ├── package.json             # Node.js dependencies
│   └── README.md                # Add-in documentation
├── start_services.ps1           # PowerShell launcher
├── start_services.bat           # Batch launcher
├── troubleshoot.ps1             # Troubleshooting script
├── SETUP_GUIDE.md               # Detailed setup guide
└── README.md                    # This file

🚀 Quick Start

Prerequisites

• Node.js (v14+)
• Python (v3.7+)
• Excel Desktop (Office 365 or Excel 2019+)

Dependencies

The project uses the following key dependencies:

Python Backend:

• Flask (Web framework)
• XGBoost (Machine learning model)
• NumPy (Numerical computing)
• scikit-learn (ML utilities)
• Flask-CORS (Cross-origin requests)

Frontend:

• React (UI framework)
• Office.js (Excel integration)
• Webpack (Build tool)

See flask_server/requirements.txt and employe_ml_excel_addin/package.json for complete dependency lists.

Installation

1. Clone the repository:

   git clone https://github.com/Mharfe23/employee-churn-predictor
   cd Excel_web_app

2. Run the automated setup:

   # Windows (PowerShell as Administrator)
   .\start_services.bat

3. Or use manual setup:

   # Install Python dependencies
   cd flask_server
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt
   
   # Install Node.js dependencies
   cd ../employe_ml_excel_addin
   npm install

4. Alternative: Install from root requirements.txt:

   # From project root directory
   pip install -r requirements.txt

Running the Application

1. Start Flask Server:

   cd flask_server
   python app.py

2. Start Excel Add-in:

   cd employe_ml_excel_addin
   npm start

3. Use in Excel:

   • Go to Home tab
   • Click "Churn Predictor" in ML Tools group
   • Load sample data and make predictions

📊 Data Format

The add-in expects employee data in this format:

Column	Type	Description	Example
satisfaction_level	0-1	Employee satisfaction score	0.38
last_evaluation	0-1	Last performance evaluation	0.53
number_project	Integer	Number of projects worked on	2
average_montly_hours	Integer	Average monthly work hours	157
time_spend_company	Integer	Years at company	3
Work_accident	0/1	Had work accident	0
promotion_last_5years	0/1	Promoted in last 5 years	0
Department	String	Department name	"sales"
salary	String	Salary level	"low"

🔧 Development

Making Changes

• Frontend: Edit employe_ml_excel_addin/src/taskpane/components/App.jsx
• Backend: Edit flask_server/app.py
• Configuration: Edit employe_ml_excel_addin/manifest.xml

Building for Production

cd employe_ml_excel_addin
npm run build

Testing

# Test Flask server
cd flask_server
python test_server.py

# Validate add-in manifest
cd employe_ml_excel_addin
npm run validate

🛠️ Troubleshooting

Run the troubleshooting script:

.\troubleshoot.ps1

Common issues and solutions are documented in SETUP_GUIDE.md.

📈 Model Performance

The XGBoost model provides:

• Accuracy: ~95% on test data
• Features: 9 employee attributes
• Output: Churn probability (between 0 and 1)

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Microsoft Office Add-ins documentation
• XGBoost library for machine learning
• React and Office.js for the frontend framework