Devices such as Jawbone Up, Nike FuelBand, and Fitbit make it possible to inexpensively collect large amounts of data about personal activity. The devices are part of the quantified self movement. This paper aims to use data from accelerometers worn on the belt, forearm, arm, and dumbell of six persons. They were asked to lift the barbell correctly and incorrectly in five different ways. I build a supervised machine learning (Random Forest) model to predict the type of movement. The model is then tested for accuracy through cross-validation.

Analyzing the on-time performance of commercial aircraft for the 2014 calendar year. This study considers two metrics for on-time performance - delays relative to target time (optimization is best for direct flights) and delays relative to scheduled arrival time (optimization is best for connecting flights). The study reports on the top 3 airlines for each scenario.

The purpose of this report is to examine the effect (if any) of transmission type (automatic vs. manual) on fuel economy. To do this we examine the mtcars data available in the datasets R library. This data set was originally published in the 1974 Motor Trend US magazine that covers fuel economy and 10 properties relating to vehicle design and performance for 32 1973-74 model automobiles. We will perform some exploratory data analysis, fit linear models, plot residuals and diagnostics, and supply data-driven conclusions on the effect of transmission type on fuel economy.

Exploring the NOAA's severe weather data sets within the United States from 1950-2011. This study will predict the number of injuries, fatalities, property damage, and crop damage resulting from severe weather events. This study was performed for Peer Assessment 2 for Coursera course Reproducible Research, and submitted for the December 21, 2014 deadline.