---
title: GitHub Developer Productivity Predictor
emoji: 🚀
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit
---

# 🚀 GitHub Developer Productivity Predictor

This is an AI-powered tool that predicts developer productivity scores based on GitHub activity metrics.

## 🎯 What it does

The model analyzes 6 key developer metrics to predict a productivity score (0-100):

- **Daily Coding Hours**: Average hours spent coding per day
- **Commits Per Day**: Average number of commits made per day  
- **Pull Requests Per Week**: Average number of pull requests created per week
- **Issues Closed Per Week**: Average number of issues resolved per week
- **Active Repositories**: Number of repositories actively contributed to
- **Code Reviews Per Week**: Average number of code reviews performed per week

## 🤖 Model Details

- **Algorithm**: Random Forest Regressor
- **Features**: 6 numeric GitHub activity metrics
- **Performance**: Trained on synthetic GitHub developer data
- **Preprocessing**: StandardScaler for feature normalization

## 🎮 How to Use

1. Enter your GitHub activity metrics in the input fields
2. Click "Predict Productivity Score" to get your score
3. Try the example buttons to see different developer profiles

## 📊 Score Interpretation

- **80-100**: 🌟 Excellent - High productivity developer!
- **70-79**: ✅ Very Good - Above average productivity!
- **60-69**: 👍 Good - Solid productivity level!
- **50-59**: ⚖️ Average - Room for improvement!
- **Below 50**: 📈 Below Average - Consider focusing on key metrics!

## ⚠️ Disclaimer

This is a demonstration model for educational purposes. Real developer productivity depends on many factors beyond GitHub metrics, including code quality, collaboration, problem-solving skills, and project complexity.

## 🛠️ Technical Stack

- **Frontend**: Gradio
- **Backend**: Python, scikit-learn
- **Model**: Random Forest Regressor
- **Deployment**: Hugging Face Spaces

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*Built with ❤️ for the developer community*