Next Word Prediction Application

Mohammed faheez

Introduction

Predictive Text Using N-Gram Modeling

This project builds a Next Word Prediction App using statistical language modeling techniques.

The model was trained on English text data from: - Twitter - News - Blogs

The objective is to predict the most probable next word given a phrase.

Problem & Data Processing

The Challenge

Given a phrase such as:

“The economy is expected to”

Predict the next most likely word.

Data Preparation

  • Converted text to lowercase
  • Removed punctuation and numbers
  • Tokenized words
  • Created:
    • Unigram model
    • Bigram model
    • Trigram model

To improve performance: - Used sampling - Kept high-frequency n-grams only - Built efficient lookup tables

Algorithm Design

N-Gram Backoff Strategy

Prediction logic:

  1. Use last two words → search Trigram
  2. If no match → search Bigram
  3. If no match → return most frequent Unigram

Benefits: - Always returns a prediction - Fast computation - Memory efficient

The word with the highest probability is selected.

The Shiny Application

How It Works

  1. User enters a phrase
  2. Clicks Predict
  3. Model processes input
  4. Displays a single predicted word

Features: - Simple interface - Fast response time - Deployed on shinyapps.io - Real-time prediction

Business Value & Future Improvements

Applications

  • Messaging apps
  • Email systems
  • Search engines
  • Customer support chatbots

Strengths

  • Lightweight statistical model
  • Low latency
  • Scalable architecture

Future Enhancements

  • 4-gram expansion
  • Advanced smoothing (Kneser-Ney)
  • Deep learning models (LSTM)
  • Personalized predictions

This project demonstrates transforming data into a deployable data product.