{"action":"closed","issue":{"url":"https://api.github.com/repos/ronan-mch/machine_learning/issues/3","repository_url":"https://api.github.com/repos/ronan-mch/machine_learning","labels_url":"https://api.github.com/repos/ronan-mch/machine_learning/issues/3/labels{/name}","comments_url":"https://api.github.com/repos/ronan-mch/machine_learning/issues/3/comments","events_url":"https://api.github.com/repos/ronan-mch/machine_learning/issues/3/events","html_url":"https://github.com/ronan-mch/machine_learning/issues/3","id":177253322,"number":3,"title":"Data visualization(s) based on suitable visualization techniques including a principal component analysis (PCA).","user":{"login":"ronan-mch","id":1145457,"avatar_url":"https://avatars.githubusercontent.com/u/1145457?v=3","gravatar_id":"","url":"https://api.github.com/users/ronan-mch","html_url":"https://github.com/ronan-mch","followers_url":"https://api.github.com/users/ronan-mch/followers","following_url":"https://api.github.com/users/ronan-mch/following{/other_user}","gists_url":"https://api.github.com/users/ronan-mch/gists{/gist_id}","starred_url":"https://api.github.com/users/ronan-mch/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ronan-mch/subscriptions","organizations_url":"https://api.github.com/users/ronan-mch/orgs","repos_url":"https://api.github.com/users/ronan-mch/repos","events_url":"https://api.github.com/users/ronan-mch/events{/privacy}","received_events_url":"https://api.github.com/users/ronan-mch/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":{"login":"AliChegini","id":14887288,"avatar_url":"https://avatars.githubusercontent.com/u/14887288?v=3","gravatar_id":"","url":"https://api.github.com/users/AliChegini","html_url":"https://github.com/AliChegini","followers_url":"https://api.github.com/users/AliChegini/followers","following_url":"https://api.github.com/users/AliChegini/following{/other_user}","gists_url":"https://api.github.com/users/AliChegini/gists{/gist_id}","starred_url":"https://api.github.com/users/AliChegini/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/AliChegini/subscriptions","organizations_url":"https://api.github.com/users/AliChegini/orgs","repos_url":"https://api.github.com/users/AliChegini/repos","events_url":"https://api.github.com/users/AliChegini/events{/privacy}","received_events_url":"https://api.github.com/users/AliChegini/received_events","type":"User","site_admin":false},"assignees":[{"login":"AliChegini","id":14887288,"avatar_url":"https://avatars.githubusercontent.com/u/14887288?v=3","gravatar_id":"","url":"https://api.github.com/users/AliChegini","html_url":"https://github.com/AliChegini","followers_url":"https://api.github.com/users/AliChegini/followers","following_url":"https://api.github.com/users/AliChegini/following{/other_user}","gists_url":"https://api.github.com/users/AliChegini/gists{/gist_id}","starred_url":"https://api.github.com/users/AliChegini/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/AliChegini/subscriptions","organizations_url":"https://api.github.com/users/AliChegini/orgs","repos_url":"https://api.github.com/users/AliChegini/repos","events_url":"https://api.github.com/users/AliChegini/events{/privacy}","received_events_url":"https://api.github.com/users/AliChegini/received_events","type":"User","site_admin":false}],"milestone":null,"comments":0,"created_at":"2016-09-15T18:31:08Z","updated_at":"2016-09-24T09:43:06Z","closed_at":"2016-09-24T09:43:06Z","body":"Touch upon the following subjects, use visualizations when it appears sensible. Keep in mind the ACCENT principles and Tufte’s guidelines when you visualize the data.\r\n\r\n- [x] Are there issues with outliers in the data,\r\n- [ ] do the attributes appear to be normal distributed,\r\n- [ ] are variables correlated,\r\n- [ ] does the primary machine learning modeling aim appear to be feasible based on your visualizations.\r\n\r\nThere are three aspects that needs to be described when you carry out the PCA analysis for the report:\r\n- [x] The amount of variation explained as a function of the number of PCA components included,\r\n- [ ] the principal directions of the considered PCA components (either find a way to plot them or interpret them in terms of the features),\r\n- [ ] the data projected onto the considered principal components.\r\n\r\nIf your attributes have very different scales it may be relevant to standardize the data prior to the PCA analysis."}}
{"actor":{"display_login":"AliChegini"}}