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Prediction model for class (how well an exercise is performed) for Weight Lifting based on sensors

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Prediction model for Weight Lifting Exercises

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 will 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.

The goal of the project is to predict the manner in which they did the exercise. This is the "classe" variable in the data set: A - E

  • exactly according to the specification (Class A),
  • throwing the elbows to the front (Class B),
  • lifting the dumbbell only halfway (Class C),
  • lowering the dumbbell only halfway (Class D)
  • throwing the hips to the front (Class E).

Project report

This report describes how I built the model, how I used cross validation, what I think the expected out of sample error is, and why I made the choices I did.

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Prediction model for class (how well an exercise is performed) for Weight Lifting based on sensors

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