This presentation introduces a Predictive Text Model designed to predict the next word in a user-provided phrase. Built using N-gram Language Modeling (unigrams, bigrams, and trigrams), the model leverages real-world text data sourced from blogs, news articles, and Twitter. The model is deployed as an interactive Shiny App that provides real-time word predictions, enhancing typing efficiency and user experience.

This analysis compares a custom U.S. Diffusion Index, based on employment, industrial production, and housing data, with the Chicago Fed National Activity Diffusion Index (CFNAIDIFF). The comparison highlights differences in national stability and regional volatility, offering insights into economic trends and sector-specific dynamics from 2010 to 2024.

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DATA 607 FALL 2024 DATA SCIENCE IN CONTEXT