Practical Machine Learning Course Project
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. In this project, we are using datasets (Weight Lifting Exercise Dataset) obtained from Human Activity Recognition using accelerometers on the belt, forearm, arm, and dumbell of 6 participants.
This Repository contains R code and the documentation of the final course project of the Practical Machine Learning on Coursera part of data science specialization.
Participatns were asked to perform barbell lifts correctly and incorrectly in 5 different ways. The goal of our project is to predict the manner in which they did the exercise or to investigate "how (well)" an activity was performed by the wearer.
Links To the Markdown and html file.
The task was to predict the type of barbell lift based on data from several accelerometers.
The file coursera-machine-learning-cp.rmd contains a description of the task and the machine-learning process with Random Forests together with the results.