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--- |
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title: Email Performance Predictor |
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emoji: 📧 |
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colorFrom: indigo |
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colorTo: green |
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sdk: gradio |
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sdk_version: "4.25.0" |
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app_file: app.py |
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pinned: false |
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--- |
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# 🚀 Email Performance Predictor - Forks Over Knives |
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An AI-powered email marketing tool that predicts email performance and provides actionable recommendations based on historical campaign data. |
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## 🎯 Features |
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- **Performance Prediction**: Predict open rates, click rates, and unsubscribe rates |
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- **Sentiment Analysis**: Analyze email sentiment using DistilBERT |
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- **Content Classification**: Categorize emails as engaging, promotional, informative, etc. |
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- **Smart Recommendations**: Get actionable tips to improve email performance |
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- **Real-time Analysis**: Instant feedback on your email content |
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## 📊 Model Performance |
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The app uses machine learning models trained on 311 email campaigns: |
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- **Click Rate Model**: Ridge Regression (R² = 0.28) |
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- **Open Rate Model**: Random Forest (R² = -0.06) |
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- **Unsubscribe Rate Model**: Random Forest (R² = -0.02) |
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*Note: Models show varying performance. Click rate predictions are most reliable.* |
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## 🛠️ How to Use |
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1. **Subject Line**: Enter your email subject line |
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2. **Preview Text**: Add preview text (optional) |
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3. **Campaign Name**: Enter your campaign name |
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4. **Day of Week**: Select when you plan to send |
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5. **Email List**: Choose your target audience |
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6. **Send Time**: Specify send time (e.g., "9:00 AM") |
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7. **Recipients**: Enter total recipient count |
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8. **Target Metric**: Choose what you want to optimize for |
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## 📈 What You Get |
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- **Performance Score**: 0-100 score based on predicted metrics |
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- **Sentiment Analysis**: Positive/negative sentiment with confidence |
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- **Content Classification**: How your email is categorized |
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- **Recommendations**: Specific tips to improve performance |
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- **Email Details**: Summary of key metrics |
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## 🔧 Technical Details |
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### Models Used |
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- **Sentiment**: DistilBERT (Hugging Face) |
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- **Classification**: BART-large-MNLI (Zero-shot) |
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- **Performance**: Custom trained models on campaign data |
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### Features Extracted |
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- Text length and word count |
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- Punctuation usage (!, ?) |
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- Emoji and number counts |
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- Capitalization ratio |
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- Send timing |
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- Audience segmentation |
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## 📝 Example Predictions |
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**High-performing email:** |
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- Subject: "Wrap Up Your Monday with Flavor 🌯🥑" |
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- Predicted Click Rate: ~1.24% |
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- Score: 85/100 |
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**Low-performing email:** |
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- Subject: "Newsletter Update" |
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- Predicted Click Rate: ~0.3% |
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- Score: 45/100 |
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## ⚠️ Limitations |
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- Models trained on limited dataset (311 campaigns) |
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- Performance varies by metric type |
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- Predictions are estimates based on historical patterns |
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- Best used as guidance alongside marketing expertise |
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## 🚀 Deployment |
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This app is designed for Hugging Face Spaces. Upload all files and it will automatically deploy. |
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### Required Files |
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- `app.py` - Main application |
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- `requirements.txt` - Dependencies |
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- `*.pkl` files - Trained models and preprocessors |
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## 📞 Support |
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For questions about the model or improvements, refer to your campaign data analysis and model training logs. |
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--- |
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*Built with Gradio, Transformers, and Scikit-learn* |
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