CLICK HERE to see report in html


UPDATE: New and adjusted specifications on the original dataset attain accuracies of over 99.9%, with precision, recall, and F1 of over 99.9% as well on all classes. See the analysis in this Jupyter Notebook, or check out the repo here.

Machine Learning on Accelerometer Data

This repository contains the R code for different machine learning algorithms. Accelerometer data is used to predict how well individuals perform weight-lifting exercises. The dataset comes from Veloso et al., (2013) and it contains data from accelerometers on the belt, forearm, arm, and dumbbell from 6 individuals.

The dataset comprises information on 6 participants who were asked to perform one set of 10 repetitions of the unilateral dumbbell biceps curl in five different ways: correctly; throwing the elbows to the front; lifting the dumbbell only halfway; lowering the dumbbell only halfway; and throwing the hips to the front. More information on the dataset can be found here.

The code in this repository processes the data and constructs 3 different machine learning algorithms, including CART, Random Forest, and Boosted (GBM) to predict the way dumbbell biceps curls were performed.

The best performing algorithm is a Random Forest specification with 99.3% accuracy, followed by a Boosted GBM with 94.3% accuracy, in the testing dataset.

The dataset is available here.



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