{"id":15520914,"url":"https://github.com/samedwardes/practicalmachinelearning","last_synced_at":"2026-01-19T22:01:42.788Z","repository":{"id":104820938,"uuid":"193834722","full_name":"SamEdwardes/practicalmachinelearning","owner":"SamEdwardes","description":"Repository for JHU Coursera Practical Machine Learning","archived":false,"fork":false,"pushed_at":"2019-07-01T23:39:52.000Z","size":9630,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"gh-pages","last_synced_at":"2025-04-06T12:13:48.574Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SamEdwardes.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-06-26T05:21:49.000Z","updated_at":"2019-07-01T23:39:54.000Z","dependencies_parsed_at":"2023-05-30T07:00:39.467Z","dependency_job_id":null,"html_url":"https://github.com/SamEdwardes/practicalmachinelearning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/SamEdwardes/practicalmachinelearning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamEdwardes%2Fpracticalmachinelearning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamEdwardes%2Fpracticalmachinelearning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamEdwardes%2Fpracticalmachinelearning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamEdwardes%2Fpracticalmachinelearning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SamEdwardes","download_url":"https://codeload.github.com/SamEdwardes/practicalmachinelearning/tar.gz/refs/heads/gh-pages","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamEdwardes%2Fpracticalmachinelearning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28587062,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-19T20:45:59.482Z","status":"ssl_error","status_checked_at":"2026-01-19T20:45:41.500Z","response_time":67,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-02T10:30:22.651Z","updated_at":"2026-01-19T22:01:42.774Z","avatar_url":"https://github.com/SamEdwardes.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Practical Machine Learning\n\nRepository for JHU Coursera Practical Machine Learning course week 4 project.\n\n- See the [index page](https://samedwardes.github.io/practicalmachinelearning/) for an overview of the selected model\n- See the [notebooks repo](notebooks) for data exploration and other models\n\nThe final model used random forests to obtain an estimated out of sample error rate of 99%. When submitting to the week 4 Coursera project quiz the predections were correct with 100% accuracy.\n\nNote the following resources were used and very helpful to the completion of the project:\n\n- [blog post on parrallel implementation](https://github.com/lgreski/datasciencectacontent/blob/master/markdown/pml-randomForestPerformance.md)\n- [blog post on html pages for github](https://github.com/lgreski/datasciencectacontent/blob/master/markdown/pml-ghPagesSetup.md)\n\n## Background\n\nUsing 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. 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).\n\n## Data\n\n### The training data for this project are available here:\n\n[https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv](https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv)\n\n### The test data are available here:\n\n[https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv](https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv)\n\nThe data for this project come from this source: http://groupware.les.inf.puc-rio.br/har. If you use the document you create for this class for any purpose please cite them as they have been very generous in allowing their data to be used for this kind of assignment.\n\n## What you should submit\n\nThe 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. You should create a report describing:\n\n- how you built your model, \n- how you used cross validation, \n- what you think the expected out of sample error is, and \n- why you made the choices you did. You will also use your prediction model to predict 20 different test cases.\n\n### Peer Review Portion\n\nYour submission for the Peer Review portion should consist of a link to a Github repo with your R markdown and compiled HTML file describing your analysis. Please constrain the text of the writeup to \u003c 2000 words and the number of figures to be less than 5. It will make it easier for the graders if you submit a repo with a gh-pages branch so the HTML page can be viewed online (and you always want to make it easy on graders :-).\n\n### Course Project Prediction Quiz Portion\n\nApply your machine learning algorithm to the 20 test cases available in the test data above and submit your predictions in appropriate format to the Course Project Prediction Quiz for automated grading.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamedwardes%2Fpracticalmachinelearning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsamedwardes%2Fpracticalmachinelearning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamedwardes%2Fpracticalmachinelearning/lists"}