I implemented a simple n-gram model for text prediction. The data consists of over 3 million tweets, blog and news posts. Using the frequencies of the bigrams, trigrams, and quadrigrams in the data, I made three n-gram prediction models. To predict the most likely next word in a given sequence, each model makes a prediction. If two or more of the n-grams models agree, I nominate that word as the most likely next word. If the models don’t agree, I use a normalized, weighted likelihood, which I calculated with the validation data.

The goal of this project is to perform exploratory analysis on the given data set, which contains blog, news and twitter posts. After analysis, we summarize our plans for a prediction model.

Presentation making a case for our Black Cherry Tree volume prediction application.

Presentation of cherry tree data, including 3D plot of the data.

Using the R `trees` dataset, a demonstration of using `plotly` to display the data on a 3D chart.

This map was created using R markdown language using the leaflet map-rendering package.

Storms and other severe weather events are increasingly causing both public health and economic problems for communities and municipalities. Using the U.S. National Oceanic and Atmospheric Administration’s storm database, which tracks characteristics of major storms and weather events in the United States, we analyzed the public health and economic impacts of severe weather from 1950 and to the end in November 2011. We calculated the public health impacts in two ways: the total number of fatalities and injuries for a given type of weather event and the fatality rate of a weather event, which is the ratio of the number of fatalities for the event and the total number of fatalities and injuries. From these data, we found that tornadoes by far caused the most fatalities and injuries, but that the cold and wind chill had the highest fatality rate. For economic impacts, we measured the total property and crop damages of a weather event. Tornadoes again had by far the highest impact.