The application predicts the most probable next word of an incomplete sentence. It also supports the user in completion of the currently typed last word. The prediction algorithm is based on an n-gram language model with interpolation and discounting techniques. The application was developed in R using the R shiny package.

This report is part of the Data Science Capstone Word Prediction Project. It’s goal is the exploratory analysis of the twitter, blogs and news text corpora. The report explains how to get, sample and preprocess the data, the steps of the analysis, and the construction of n-grams. Main features are illustrated in various plots and tables. The report ends with interesting findings and the plans for creating the prediction algorithm and Shiny application to enable next word prediction of an arbitrary text.

An Application to explore the development of movie budgets between 1900 and 2005

test plot

An Application to explore the development of movie budgets between 1900 and 2005

This study used data from wearable device sensors to predict human activity. A combined model (randomForest, gbm, treebag) was used to achieve an estimated out-of-sample error on the cross-validation dataset of less than 1%.

In this report we show which severe weather events have the greatest impact on public health and economy. The study is based on data from the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database (wheather events from 1950 to 2011). That database contains data of major storms and weather events, when and where they occur, and what damage (injuries, fatalities, property, crop) was generated. The top 10 most damaging events for each catagory were calculated. We found, that with the period from 1950 to 2011 Tornados had the strongest impact on public health (causing about 5500 fatalities and 90000 injuries). Floods caused the most damage on economy (180 billion $).

Sedentary activity, such as TV watching, is associated with negative changes in many aspects of health. Researchers concluded that increasing public awareness of alternatives to TV watching and reducing barriers to alternative activities that are more socially and physically engaging could reduce TV use by older people and diminish the potential for associated negative health effects. But is it really the case that older people have a higher TV consumption? Do the GSS data support those statistics? This study will try to answer the question whether retired people watch more TV than not-retired.