Word Predictor is a shiny application that runs on shinyapps.io and takes as input a phrase (multiple words) in a text box input and outputs a prediction of the next word. It predicts the most likely next word, based on frequently occurring phrases (n-grams).

This report is part of the Coursera John Hopkins Data Science Capstone Project. The course starts with the basics, analyzing a large corpus of text documents to discover the structure in the data and how words are put together. It covers cleaning and analyzing text data, then building and sampling from a predictive text model. Finally, a predictive text product will be built. This milestone report provides a first stage in the project providing data acquisition and cleaning, and exploratory analysis of the course data set.

Application is for consulting the availability of bikes in the bike sharing system of Malaga City

This report explores the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database to investigate the types of severe weather events that have the largest impact to public health, and those types that result on property and crop damage. This study focus on the 1994-2011 period in which more complete records are kept for severe weather events. Also, this study is based on aggregation of weather events in categories to allow a more consistently view of data. From this study it can be concluded that heat and storm related weather events are the most dangerous to people, while rain and heat are the most costly event type categories to the economy.