Author: Your Name
Technology: R, Shiny, NLP
This project demonstrates a simple predictive text system that suggests the next word based on user input.
Predictive text systems are used in search engines, mobile keyboards, and messaging platforms.
The goal is to predict the most likely next word based on the words already typed by the user.
This project focuses on building a lightweight, fast, and easy-to-use predictive model that can be accessed through a web interface.
The application uses Natural Language Processing techniques.
Text data is first cleaned and tokenized into individual words.
Word sequences (n-grams) are analyzed to learn common word patterns.
When a user enters a phrase, the model examines the last word and predicts the most probable next word based on learned frequencies.
This approach prioritizes simplicity and speed while demonstrating core NLP concepts.
The user enters a phrase containing multiple words into a text input box.
After clicking the predict button, the server processes the input.
The model analyzes the last word in the phrase and returns a single predicted next word.
The application is deployed on shinyapps.io and can be accessed from any modern web browser.
The application is intuitive and responds quickly with a prediction.
It provides a clear demonstration of how predictive text models work in practice.
Although the model is simple, it can be extended using larger datasets and more advanced language models.
This project highlights end-to-end data product development, including modeling, deployment, and presentation.