The prediction engine uses N-gram language modeling with backoff.

How it works

• Trigrams (3 words) → primary prediction
• Bigrams (2 words) → fallback if trigram not found
• Default word → used when no match exists

Key design choice

• Frequency-based prediction for speed and simplicity

The app is designed for real-time interaction.

User Flow

1. ✍️ Enter a short phrase
2. ⚙️ The model analyzes recent words
3. 🔮 The next word is predicted instantly

Example
Typing: How are
Prediction: you

Application Highlights

• ⚡ Fast predictions using preloaded .RData tables
• 🧠 Lightweight model optimized for memory efficiency
• 🎨 Clean and minimal Shiny user interface

Performance Focus

• Reduced lookup tables
• No runtime model training
• Immediate response on input change

What this project shows

• Practical use of NLP in a data product
• Effective balance between accuracy and performance
• Successful deployment as an interactive Shiny app

Future Enhancements

• Probability-based ranking
• Smoothing techniques
• Deployment to mobile platforms

🔗 Live Demo
(https://mohammedadnan.shinyapps.io/PredictorApp/)