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, our 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: link (see the section on the Weight Lifting Exercise Dataset).
The training data for this project are available here: link.
The test data are available here: link.
The data for this project come from this source: link.
The goal of our project is to predict the manner in which the participants did the exercise. This is the "classe" variable in the training set. Any of the other variables may be used to predict the "classe". We will create a report describing how we built our model, how we used cross validation, what we think the expected out of sample error is, and why we made the choices we did. We will also use our prediction model to predict 20 different test cases.
The submission for the Peer Review portion should consist of a link to a Github repo with R markdown and compiled HTML file describing the analysis. The text of the writeup must be constrained to < 2000 words and the number of figures to be less than 5. The repo should be submitted with a gh-pages branch so the HTML page can be viewed online.
The machine learning algorithm is to be applied to the 20 test cases available in the test data. The results are to be submitted to the Course Project Prediction Quiz for automated grading.