Predictive Text Challenge

• Users type millions of messages daily.
• Faster typing improves productivity.
• Intelligent prediction reduces keystrokes.
• Goal: Build a next-word prediction system using NLP.

Business Value

• Better user experience
• Faster communication
• Increased engagement

N-Gram Language Model

The prediction engine uses:

• Data cleaning
• Tokenization
• Unigrams
• Bigrams
• Trigrams

Workflow:

1. User enters text
2. Last words are extracted
3. Matching n-grams are searched
4. Most probable next word is returned

Shiny Web App

Features:

• Simple interface
• Real-time prediction
• Fast response
• Easy to use

User enters a phrase and clicks Predict.

The application immediately displays the predicted next word.

Example Predictions

Benefits:

• Quick predictions
• Lightweight model
• Easy deployment

Roadmap

• Larger training datasets
• Deep learning language models
• Personalized predictions
• Multi-language support

Conclusion

The project demonstrates how NLP can power intelligent text prediction systems and improve user productivity.