Coursera course: Practical Machine Learning
Github Page For Project: https://github.com/Emaasit/MachineLearning
gh-page branch for Project Report: http://emaasit.github.io/MachineLearning/predictionAssignment.html
Note that http://emaasit.github.io/MachineLearning/predictionAssignment.html will be redirected to http://www.danielemaasit.com/MachineLearning/predictionAssignment.html
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: 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
The test data are available here:
https://d396qusza40orc.cloudfront.net/predmachlearn/pml-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 our project is to predict the manner in which they did the exercise. This is the "classe" variable in the training set. We also created a report describing how we built our model, how we used cross validation, and what we think the expected out of sample error is, and why we made the choices we did. We also use our prediction model to predict 20 different test cases.