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---
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title: GitHub Developer Productivity Predictor
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emoji: 🚀
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colorFrom: blue
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colorTo: purple
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sdk: gradio
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sdk_version: 4.44.0
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app_file: app.py
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pinned: false
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license: mit
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---
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# 🚀 GitHub Developer Productivity Predictor
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This is an AI-powered tool that predicts developer productivity scores based on GitHub activity metrics.
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## 🎯 What it does
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The model analyzes 6 key developer metrics to predict a productivity score (0-100):
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- **Daily Coding Hours**: Average hours spent coding per day
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- **Commits Per Day**: Average number of commits made per day
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- **Pull Requests Per Week**: Average number of pull requests created per week
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- **Issues Closed Per Week**: Average number of issues resolved per week
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- **Active Repositories**: Number of repositories actively contributed to
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- **Code Reviews Per Week**: Average number of code reviews performed per week
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## 🤖 Model Details
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- **Algorithm**: Random Forest Regressor
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- **Features**: 6 numeric GitHub activity metrics
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- **Performance**: Trained on synthetic GitHub developer data
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- **Preprocessing**: StandardScaler for feature normalization
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## 🎮 How to Use
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1. Enter your GitHub activity metrics in the input fields
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2. Click "Predict Productivity Score" to get your score
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3. Try the example buttons to see different developer profiles
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## 📊 Score Interpretation
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- **80-100**: 🌟 Excellent - High productivity developer!
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- **70-79**: ✅ Very Good - Above average productivity!
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- **60-69**: 👍 Good - Solid productivity level!
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- **50-59**: ⚖️ Average - Room for improvement!
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- **Below 50**: 📈 Below Average - Consider focusing on key metrics!
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## ⚠️ Disclaimer
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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.
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## 🛠️ Technical Stack
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- **Frontend**: Gradio
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- **Backend**: Python, scikit-learn
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- **Model**: Random Forest Regressor
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- **Deployment**: Hugging Face Spaces
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---
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*Built with ❤️ for the developer community* |
