Desarrollar habilidades básicas para manejo de información y comandos en R.

This is the first assignment

This project is part of the Johns Hopkins University Data Science Specialization Capstone. The objective is to explore large-scale English text datasets (blogs, news, and Twitter) and build the foundation for a next-word prediction model similar to those used in mobile smart keyboards. The analysis includes: Basic dataset summaries (file size, line counts, maximum line length) Exploratory analysis of text structure Sampling and cleaning strategies suitable for large corpora Word frequency analysis (unigrams and bigrams) Distribution of words per line Vocabulary coverage analysis (50% and 90% token coverage) Preliminary modeling strategy for n-gram backoff prediction The results highlight differences between text sources (short Twitter messages vs. long blog entries), motivate efficient sampling techniques, and inform the design of a responsive Shiny application for deployment. The next phase of the capstone will implement an optimized n-gram model with a backoff strategy and deploy it via Shiny for real-time next-word prediction.

This project demonstrates a next word prediction model using a bigram language model implemented in R.

my report

Scripts with analyses reported in the manuscript "Stimulus Presentation Duration Affects Category-Learning Accuracy", by Fotis A. Fotiadis, Iris Antonatou Stamatopoulou, & Argiro Vatakis