Predicting Activity of users from their accelerometer readings
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. 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, we are going to use data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants. The participants were asked to perform barbell lifts correctly and incorrectly in 5 different ways. We are going to compare various machine learning algorithm to predict the type of exercise
Disclaimer
This analysis was done as a course project for the 'Practical Machine Learning' course which is part of the Data Science Specialisation provided by Johns Hopkins Bloomberg School of Public Health via Coursera.