On this case study we develop a model to predict the next word you will type, in real-time, based on Markov Chains. This model is based on the assumption that the probability of a word depends on previous words already in a sentence (a Markov assumption). Markov models are the class of probabilistic models that assume we can predict the probability of some future unit without looking too far into the past

The goal of this project is to explain major features of the data we have identified and briefly summarize our plans for creating the prediction algorithm for the next English word you will type.

People regularly quantify how much of a particular activity they do, but they rarely quantify how well that same activity is performed. This analysis predicts how well people exercise based on data produced by accelarators attached to their belt, forearm, arm, and dumbell.

This application demonstrates ad hoc data investigation of carbon related features in data extracted from the 1974 issue of Motor Trend, an US automobile magazine. The data comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models)

The basic goal of this study is to explore the NOAA Storm Database and answer some basic questions about health and economic impact of severe weather events across the United State