Weight Lifting Exercise - Multiclass Classification

February 28, 2017

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Here, I use Weight Lifting Exercise dataset and implement a random forest model to predict the manner in which a person does exercise.
## Overview In this project, I implement various classification models to predict the manner in which people do exercise, based on accelerometer data from devices like Jawbone Up. I perform the machine learning experiments in two different environments: (i) AzureML and (ii) RStudio. ## 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. The goal of the project is to predict the manner in which they did the exercise. This is the \\\"classe\\\" variable in the training set. 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 The data for this project come from this source: http://groupware.les.inf.puc-rio.br/har.