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