A presentation on simple linear regression

Pilot A for 251

This project demonstrates the end-to-end development of a data-driven application using R and Shiny. The goal is to build an interactive Next Word Predictor powered by an N-gram language model with a backoff strategy. The application processes text data from the Reuters crude oil dataset, cleans and tokenizes it, and constructs unigrams, bigrams, and trigrams to predict the most likely next word in a user-provided phrase. The model prioritizes trigram matches for context, falls back to bigrams when necessary, and defaults to unigrams for general predictions. To quantify uncertainty, the app calculates entropy, providing users with a measure of prediction confidence. The Shiny interface allows users to input text, view top predictions, and explore visualizations such as word frequency charts, bigram and trigram plots, and word clouds.

This project demonstrates the end-to-end development of a data-driven application using R and Shiny. The goal is to build an interactive Next Word Predictor powered by an N-gram language model with a backoff strategy. The application processes text data from the Reuters crude oil dataset, cleans and tokenizes it, and constructs unigrams, bigrams, and trigrams to predict the most likely next word in a user-provided phrase. The model prioritizes trigram matches for context, falls back to bigrams when necessary, and defaults to unigrams for general predictions. To quantify uncertainty, the app calculates entropy, providing users with a measure of prediction confidence. The Shiny interface allows users to input text, view top predictions, and explore visualizations such as word frequency charts, bigram and trigram plots, and word clouds.