Consequences of Severe Weather Events on the U.S. Population Health and Economy

Data Science Projects Visualization

Synopsis

This report describes the harmful impact of severe weather events on the American population health and economy. To study the top weather events, we obtained the data from the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. NOAA keeps records of fatalities, injuries as well as estimates on property and crop damage. We specifically look at the years 1995 to 2011 because earlier years’ data is largely incomplete. From the storm database, we do an analysis to extract the top 10 severe weather events affecting population health and the economy. Our results show that severe events during warm climate months and storm seasons have the greatest impact on population health and the economy. Flooding, excessive heat and tornadoes are such examples. We also found that the economic consequences are significantly higher for properties than crops, as expected.
Read More “Consequences of Severe Weather Events on the U.S. Population Health and Economy” →

Parallelize Machine Learning in R with Multi-Core CPUs

Data Science Machine Learning

R supports parallel computations with the core parallel package. What the doParallel package does is provide a backend while utilizing the core parallel package. The caret package is used for developing and testing machine learning models in R. This package as well as others like plyr support multicore CPU speedups if a parallel backend is registered before the supported instructions are called.

The train instruction of the caret package has built-in support for parallel backends, but you have to call and set it up. If you don’t register a backend, train will resort to single-core computations. With a registered parallel backend, any caret model training will use multi-cores of the CPU, since by default the trainControl argument is already set as allowParallel=TRUE.
Read More “Parallelize Machine Learning in R with Multi-Core CPUs” →