Final Capstone Project for the Johns Hopkins Data Science Specialization. This presentation introduces a lightweight Natural Language Processing (NLP) application designed to predict the next word in a sequence using a Katz Back-off N-gram model trained on the HC Corpora dataset.

This report provides a comprehensive exploratory analysis of the HC Corpora dataset (Twitter, Blogs, and News). It details the data cleaning process, provides summary statistics for the corpora, and visualizes word frequencies (N-grams). Finally, it outlines the roadmap for building a Katz Back-off prediction algorithm and a final Shiny application.

A 5-slide reproducible pitch for a Shiny application predicting vehicle MPG, created for the Developing Data Products course.

This report analyzes the NOAA Storm Database to identify the types of weather events that are most harmful to population health and have the greatest economic consequences across the United States.