Predicting Exercise Technique Using Random Forest Algorithms

Predicting Exercise Technique Using Random Forest Algorithms

November 17, 2023


The following is my submission for the Practical Machine Learning course project under John Hopkins University’s 10-course Data Science Specialization. From the prompt of the assignment:

“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 dumbbell of 6 participants. They were asked to perform barbell lifts correctly and incorrectly in 5 different ways (…) The goal of your project is to predict the manner in which they did the exercise. This is the ‘classe’ variable in the training set. You may use any of the other variables to predict with.”


The data used for this project was sourced from the Human Activity Recognition dataset (Ugulino et al, 2012):

NOTE: The link above has not worked for me and may be outdated/unmaintained as this course is now around a decade old. With that being said, there is a copy hosted on UC Irvine’s Machine Learning Repository with more information linked here.


Click the link below to view the full RPubs writeup/report for this project with all of the included code