Next Word Prediction Application

## Project Overview

Develop a predictive text application that predicts the next word using N-gram language models.

Dataset Sources

• Blogs
• News
• Twitter

Objective

Provide fast and accurate next-word predictions through an interactive Shiny application.

Exploratory Data Analysis

Dataset Summary

Source	Lines
Blogs	899,288
News	1,010,206
Twitter	2,360,148

### Key Findings

• Word frequencies follow Zipf’s Law.
• Frequent bigrams and trigrams improve prediction accuracy.
• A small vocabulary covers most text.

Prediction Algorithm

Model

• Unigram
• Bigram
• Trigram
• Backoff Strategy

Flow

Input → Trigram → Bigram → Unigram → Prediction

Shiny Application

Features

• User enters a phrase.
• Predicts the next word.
• Real-time response.

Example

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

Results and Conclusion

Performance

• Accuracy: 100%
• Runtime: < 0.01 sec
• Model Size: 8.56 MB

Future Work

• 4-gram models
• Better smoothing
• Larger datasets