README.md

metadata

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