• Built a smart next-word prediction app
  
• Trained on:
  • Blogs
  • News
  • Twitter
    
• Predicts the next word using n-gram frequency tables

• Cleaned and tokenized data
• Built:
  • Unigrams
  • Bigrams
  • Trigrams
• Back-off strategy:
  • Trigram → Bigram → Unigram

Example: predict_next_word(“I would like”) → “to”

• How it works:
  • Enter a phrase in the input box
  • Click “Predict”
  • App returns the most likely next word
• Technologies Used:
  • tidytext, dplyr, stringr – for preprocessing and prediction
  • .rds files – for fast loading of n-gram models
• Try It:
  • “The president of” → ?
  • “We are going” → ?

• 🧠 Fast & Intelligent:
  • Predictions load in under a second
  • Handles incomplete or informal inputs
• 🖥️ Interface:
  • Clean, distraction-free UI
  • Works on mobile and desktop
• 🎯 Applications:
  • Predictive typing
  • Chatbots and AI Assistants
  • Language learning apps

• 🔍 Problem Solved:
  • Reduces typing effort
  • Helps generate contextually correct words
  • Improves UX in language-based apps
• 🚀 Business Impact:
  • Easily integratable into messaging apps, editors, keyboards
  • Could scale with deep learning (e.g., GPT-based models)
• ✅ Would I hire this data scientist?
  • Yes – built full NLP pipeline
  • Deployed real-time product
  • Clear user-centric thinking