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R Projects: Weight Lifting: Predicting technique error

Using devices such as Jawbone Up, Nike FuelBand, and Fitbit it is now possible to collect a large amount of data about personal activity which allows to quantify how much of a particular activity has been done. However, the quality or way of how well a particular activity was performed is rarely evaluated. In this project, I use data from accelerometer sensors mounted on the belt, forearm, arm, and dumbbell of 6 participants who were performing barbell lifts correctly and incorrectly in 5 different ways.

The goal is to predict the manner in which they did the exercise which is represented in the classe variable in the training set. This variable is omitted in the test set. This report is describing how the model was built, how cross validation was used, what the expected out-of-sample error is, and what choices have been made. The prediction model derived in this report is finally used to predict 20 test cases.


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