How the model generates predictions

The application is powered by a statistical n-gram language model.

Core idea - Training text is split into word sequences - The model learns which words most frequently follow others - For a given input, the model: - Extracts the last word - Predicts the most likely next word

✔ Simple
✔ Interpretable
✔ Designed for real-time use

What the model does well

• Trained on Twitter / News-style text
• Uses frequency-based ranking
• Returns a prediction whenever a known pattern exists

Performance characteristics - Strong accuracy for common phrases - Lower accuracy for rare or unseen inputs - Optimized for speed and reliability

⏱ Average response time: under 1 second

How users interact with the app

1. Enter a short phrase
   
2. Click Predict
   
3. Instantly receive the next word

Design focus - Clean, minimal interface - No technical knowledge required - Model is preloaded for fast response

🔗 Live Application
https://nkge21-prajjwal-shrimal.shinyapps.io/wordpredict/

Why this product matters

• Demonstrates a complete data product
• Applies Natural Language Processing in practice
• Balances accuracy, speed, and deployability

Future improvements - Trigram back-off modeling - Multiple predictions with probabilities - Larger training dataset

Takeaway:
This project shows how a simple predictive model can be transformed into a
usable, deployable application.