{"action":"opened","issue":{"url":"https://api.github.com/repos/MattD18/Quality-of-NYC-Hospitals-/issues/9","repository_url":"https://api.github.com/repos/MattD18/Quality-of-NYC-Hospitals-","labels_url":"https://api.github.com/repos/MattD18/Quality-of-NYC-Hospitals-/issues/9/labels{/name}","comments_url":"https://api.github.com/repos/MattD18/Quality-of-NYC-Hospitals-/issues/9/comments","events_url":"https://api.github.com/repos/MattD18/Quality-of-NYC-Hospitals-/issues/9/events","html_url":"https://github.com/MattD18/Quality-of-NYC-Hospitals-/issues/9","id":187381670,"number":9,"title":"Peer Midterm Review","user":{"login":"HaotianLiu","id":17007069,"avatar_url":"https://avatars.githubusercontent.com/u/17007069?v=3","gravatar_id":"","url":"https://api.github.com/users/HaotianLiu","html_url":"https://github.com/HaotianLiu","followers_url":"https://api.github.com/users/HaotianLiu/followers","following_url":"https://api.github.com/users/HaotianLiu/following{/other_user}","gists_url":"https://api.github.com/users/HaotianLiu/gists{/gist_id}","starred_url":"https://api.github.com/users/HaotianLiu/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/HaotianLiu/subscriptions","organizations_url":"https://api.github.com/users/HaotianLiu/orgs","repos_url":"https://api.github.com/users/HaotianLiu/repos","events_url":"https://api.github.com/users/HaotianLiu/events{/privacy}","received_events_url":"https://api.github.com/users/HaotianLiu/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2016-11-04T16:39:38Z","updated_at":"2016-11-04T16:39:38Z","closed_at":null,"body":"  The project is to provide an estimate of the length of stay in a hospital given a patient's medical conditions and some other basic information. The dataset comes from SPARCS and includes basic information about all patient discharged from a New York hospital in 2012. Moreover, it has the level of severity of illness scaled from 1 to 4  and an ordinal field for risk of mortality.  The preliminary models aim to examine cases of heart attack and predict the outcome of certain patients. \r\n   Things I like about this project so far/Things to improve:\r\n   1. The data cleaning process is definitely crucial to running the model.  The one-hot encoding you have done makes regression possible and converting nominal values to numeric values are helpful to achieve reasonable interpretations of your regression result.\r\n      On the other hand,  I'd like to see more analysis on the outlier that you cleaned up(the 120+) .Outliers can contain information essential to the analysis. For instance, is there any combination of features (interaction between features for instance) that is unique for this outlier? Could there be an unexpected factor that could account for this outlier? It is worth researching\r\n    2.  Feature selection is done pretty well in running model. I like the idea of choosing the statistically significant features. Also, it is a good idea to use cross-validation as well as bootstrapping to decrease the variance. \r\n         I am curious as to what is the criteria for \"statistically significant\" in your model. Is it a p-value for the features? R-square(adjusted)? And what does this statistical significance tells the readers? It would be better if you can include this information in future reports.\r\n  3.  The idea of reducing variance to a model is good and you state that you will try different regularizers in the future.  However, one piece of advice before diving too deep into variance reduction: try to plot your features against the output space. I have been experiencing persisting high variance problem for my project as well. However, when I plot the data, it turns out that some of the features are not linear to the output variable and they may require a feature transformation(to quadratic for instance). \r\n   **Overall, this is a great report and good luck on your project +1: **"}}
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