Coursera - Practical Machine Learning course assignment

Background

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, your 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:

Class A: exactly according to the specification
Class B: throwing the elbows to the front
Class C: lifting the dumbbell only halfway
Class D: lowering the dumbbell only halfway
Class E: throwing the hips to the front

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).

Data

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

Goal

The goal of your project is to predict the manner in which they did the exercise. This is the "classe" variable in the training set. You may use any of the other variables to predict with.

Report

You should create a report describing:

how you built your model,
how you used cross validation,
what you think the expected out of sample error is,
and why you made the choices you did.

You will also use your prediction model to predict 20 different test cases.

Your submission should consist of a link to a Github repo with your R markdown and compiled HTML file describing your analysis. You should also apply your machine learning algorithm to the 20 test cases available in the test data above.

Preview the final compiled html report here:
http://htmlpreview.github.io/?https://github.com/paesibassi/PracticalMachineLearning/blob/master/course_assignment.html

or here:
http://paesibassi.github.io/PracticalMachineLearning/course_assignment.html