🔍 The Problem

Typing on mobile devices is slow, repetitive, and error-prone. Predictive text systems can significantly improve typing efficiency by suggesting the next word in real-time.

🎯 The Goal

To build a lightweight, fast, and accurate next-word prediction model trained on real-world English language data from blogs, news articles, and tweets — and to provide a simple web-based interface for live user input.

⚙️ The Approach

  • Data Source: English corpora from blogs, news, and Twitter

  • Processing: Text cleaning, tokenization, and stopword removal

  • Modeling: Built n-gram frequency tables (unigram, bigram, trigram)

  • Prediction Logic: Used a Stupid Backoff strategy

    • Try trigram (last 2 words)
    • Fall back to bigram (last 1 word)
    • Else return the most common unigram

💡 The Application

  • Built with Shiny in R
  • User enters a phrase into a text box
  • App instantly predicts and displays the most likely next word
  • Hosted online for public access

App Link: https://yourname.shinyapps.io/typesmart

📈 Results & Benefits

  • The model successfully predicts contextually relevant words
  • The app is fast and easy to use
  • Demonstrates real-world application of NLP techniques in R