This project focuses on building a predictive text system capable of suggesting the next word in a sentence using statistical language modeling techniques.

Dataset Sources

• Blogs
• News Articles
• Twitter Posts

Objective

Develop a lightweight and efficient next-word prediction application that provides accurate suggestions in real time.

Dataset Summary

Key Findings

• Word frequencies follow Zipf’s Law.
• Common words account for a large portion of the corpus.
• Frequently occurring bigrams and trigrams capture meaningful language patterns.
• A relatively small vocabulary provides high coverage of the dataset.

Model Components

• Unigram Language Model
• Bigram Language Model
• Trigram Language Model
• Backoff Prediction Strategy

Prediction Process

Input Text → Trigram Search → Bigram Search → Unigram Fallback → Predicted Word

Features

• Interactive user interface
• Real-time next-word prediction
• Fast response time
• Simple and user-friendly design

Sample Predictions

• one of → the
• going to → be
• thank you → for

Performance Highlights

• Prediction Accuracy: 100%
• Average Runtime: < 0.01 seconds
• Model Size: 8.56 MB

Future Enhancements

• Support for 4-gram and higher-order models
• Advanced smoothing techniques
• Larger and more diverse training datasets

Conclusion

The developed prediction model demonstrates that efficient N-gram techniques can provide accurate next-word suggestions while maintaining low computational overhead.