{"action":"opened","issue":{"url":"https://api.github.com/repos/ctteng/HW2/issues/1","labels_url":"https://api.github.com/repos/ctteng/HW2/issues/1/labels{/name}","comments_url":"https://api.github.com/repos/ctteng/HW2/issues/1/comments","events_url":"https://api.github.com/repos/ctteng/HW2/issues/1/events","html_url":"https://github.com/ctteng/HW2/issues/1","id":60583976,"number":1,"title":"HW 2 Feedback","user":{"login":"alexchao56","id":5111035,"avatar_url":"https://avatars.githubusercontent.com/u/5111035?v=3","gravatar_id":"","url":"https://api.github.com/users/alexchao56","html_url":"https://github.com/alexchao56","followers_url":"https://api.github.com/users/alexchao56/followers","following_url":"https://api.github.com/users/alexchao56/following{/other_user}","gists_url":"https://api.github.com/users/alexchao56/gists{/gist_id}","starred_url":"https://api.github.com/users/alexchao56/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/alexchao56/subscriptions","organizations_url":"https://api.github.com/users/alexchao56/orgs","repos_url":"https://api.github.com/users/alexchao56/repos","events_url":"https://api.github.com/users/alexchao56/events{/privacy}","received_events_url":"https://api.github.com/users/alexchao56/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":0,"created_at":"2015-03-10T23:01:54Z","updated_at":"2015-03-10T23:01:54Z","closed_at":null,"body":"- I think you forgot to use K-Nearest Neighbors to analyze the dataset. See solutions for an example\r\n- Good work reassigning the labels to numbers so that you could easily compare. \r\n- There are other ways to calculate the accuracy of your model. For instance most models will have a score function. i.e. \"clf.score(X_test, y_test)\"\r\n- But what you wrote essentially does that albeit in a more manual way. It's good practice to write out the entire calculation so that you know the internals behind functions. \r\n- Check out the solutions note about scaling. Turns out that if you scale your variables before running kmeans, you can get much better predictive accuracy!\r\n- Also add comments or a short writeup comparing the models you've done. \r\n- And while the problem didn't specify it, I think using a few basic histograms to visualize the distribution of the data can go a long way in catching things like the differing scales and other peculiarities with which you can modify your model.\r\n- All in all good work! "}}