Using devices such as Jawbone Up, Nike FuelBand, and Fitbit it is now possible to collect a large amount of data about personal activity relatively inexpensively. These type of devices are part of the quantified self movement – a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. One thing that people regularly do is quantify how much of a particular activity they do, but they rarely quantify how well they do it. In this project, my goal will be to use data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants. They were asked to perform barbell lifts correctly and incorrectly in 5 different ways. More information is available from the website here: http://groupware.les.inf.puc-rio.br/har (see the section on the Weight Lifting Exercise Dataset).
The training data for this project are available here:
https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv, (also uploaded in this repo as 'training.csv').
The test data are available here:
https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv, (also uploaded in this repo as 'testing.csv').
The data for this project come from this source: http://groupware.les.inf.puc-rio.br/har. If you use the document you create for this class for any purpose please cite them as they have been very generous in allowing their data to be used for this kind of assignment.
The goal of my project is to predict the manner in which they did the exercise. This is the "classe" variable in the training set. My report described how I built my model, how I used cross validation, what I think the expected out of sample error is, and why I made the choices you did. I will also use my prediction model to predict 20 different test cases.
HTML version of my report can be viewed here.