Introduction

Context-Aware Next Word Prediction

  • Predicts the next word from user input
  • Built using NLP techniques
  • Uses n-gram language models
  • Designed for Shiny deployment

Dataset & Exploratory Analysis

Dataset Sources

  • Customer Support Data
  • Documentation Data
  • Journaling Data

Analysis Performed

  • Word frequency analysis
  • Bigram and trigram analysis
  • Data cleaning and preprocessing
  • Visualization of language patterns

Prediction Algorithm

N-Gram Back-Off Model

Trigram Prediction

Uses previous two words

Bigram Prediction

Fallback when trigram unavailable

Unigram Prediction

Final fallback strategy

Benefits

  • Fast prediction
  • Lightweight model
  • Efficient memory usage

Shiny Application

Features

  • Text input interface
  • Real-time prediction
  • User-friendly design
  • Fast response time

Example

Input:

Machine learning is

Prediction:

important

Conclusion

Project Summary

  • Successfully built a next-word prediction system
  • Applied NLP and statistical language modeling
  • Developed a deployable Shiny application
  • Optimized for runtime and memory efficiency

Future Improvements

  • Deep learning integration
  • Improved prediction accuracy
  • Larger training datasets