This project presents a Next Word Prediction App built using R Shiny. The purpose of the app is to predict the most likely next word based on a phrase entered by the user. Such models are commonly used in applications like search engines, mobile keyboards, and messaging platforms to improve typing efficiency and user experience.
The app uses a statistical language modeling approach trained on large text datasets and is deployed online for public access.
The app uses an n-gram language model, which predicts the next word based on the previous words in a phrase.
Unigrams: single-word frequencies
Bigrams: two-word sequences
Trigrams: three-word sequences
A backoff strategy is applied:
The model first attempts to match trigrams
If no match is found, it backs off to bigrams
If needed, it finally uses unigrams
The next word with the highest observed frequency is selected as the prediction.
The Shiny app provides a simple and intuitive interface:
A text input box for entering a phrase
A submit button to generate the prediction
A single predicted word displayed as output
The app responds quickly and is easy to use, even for users with no technical background. Its lightweight design ensures fast performance with minimal delay.
The app demonstrates a well-implemented and practical approach to natural language processing using n-gram models. While the method is not novel, it is effective and appropriate given the project scope.
This project shows strong skills in:
Data preprocessing
Language modeling
Shiny app development
Model deployment
Based on the quality of the implementation and presentation, this is a solid demonstration of applied data science skills, and the developer would be a strong candidate for a data science role in a startup environment.