{"action":"created","issue":{"url":"https://api.github.com/repos/JestonBlu/Neighbor-Works/issues/16","repository_url":"https://api.github.com/repos/JestonBlu/Neighbor-Works","labels_url":"https://api.github.com/repos/JestonBlu/Neighbor-Works/issues/16/labels{/name}","comments_url":"https://api.github.com/repos/JestonBlu/Neighbor-Works/issues/16/comments","events_url":"https://api.github.com/repos/JestonBlu/Neighbor-Works/issues/16/events","html_url":"https://github.com/JestonBlu/Neighbor-Works/issues/16","id":222025749,"number":16,"title":"Taxi Analysis","user":{"login":"JestonBlu","id":3339909,"avatar_url":"https://avatars2.githubusercontent.com/u/3339909?v=3","gravatar_id":"","url":"https://api.github.com/users/JestonBlu","html_url":"https://github.com/JestonBlu","followers_url":"https://api.github.com/users/JestonBlu/followers","following_url":"https://api.github.com/users/JestonBlu/following{/other_user}","gists_url":"https://api.github.com/users/JestonBlu/gists{/gist_id}","starred_url":"https://api.github.com/users/JestonBlu/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/JestonBlu/subscriptions","organizations_url":"https://api.github.com/users/JestonBlu/orgs","repos_url":"https://api.github.com/users/JestonBlu/repos","events_url":"https://api.github.com/users/JestonBlu/events{/privacy}","received_events_url":"https://api.github.com/users/JestonBlu/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":62,"created_at":"2017-04-16T18:43:09Z","updated_at":"2017-04-24T19:53:14Z","closed_at":null,"body":"@NancyDrew484 @nitroys @rmglazner \r\n\r\nI spent a good bit of time this weekend looking at the taxi cab data and I have come across an issue with the data. I dont think we are going to be able to use logistic regression to predict if the cab drivers was tipped. When I looked at the data I noticed that there is not tip data for almost anyone who paid cash for the fare. I confirmed this by looking at the documentation.\r\n\r\n![image](https://cloud.githubusercontent.com/assets/3339909/25073205/3a844a58-22a7-11e7-9e64-1451055f44e5.png)\r\n\r\nI fit some preliminary models, one using logistic regression with the response equal to a binary indicator if the cab driver was tipped with cash customers removed and I also tried doing binomial regression where the response is the proportion of tip to the total fare. Both models were worthless.  \r\n\r\nI did Shannon's original idea and fit a linear regression model on the amount of tip and the fit was much better, but with an r^2 in the 50s so still not great. So far I have just used the following predictors: \r\n\r\n* month\r\n* rate_code\r\n* pickup_time\r\n* passenger_count\r\n* trip_distance\r\n* fare_amount\r\n* toll (binary indicator)\r\n\r\nAt this point would recommend regular linear regression, although we might try a few other methods that Dr. Akleman has gone over.\r\n\r\nProbably the most interesting thing about this data is the location data.  Ive been messing around with some of the pick up and drop off locations to see if there are any significant differences that we could investigate.\r\n\r\nI was thinking that if we could break the locations down into high level districts, then maybe we could use pick up or drop off locations as predictors and then potentially try logistic regression again.\r\n\r\nHere are a few density plots I have made with the taxi data on top of  a map of New York\r\n\r\n![image](https://cloud.githubusercontent.com/assets/3339909/25073346/100ffaa8-22aa-11e7-9e46-c9fb8beaea68.png)\r\n\r\n![image](https://cloud.githubusercontent.com/assets/3339909/25073353/492a3510-22aa-11e7-9164-0a5ca687d43b.png)\r\n\r\n\r\n"},"comment":{"url":"https://api.github.com/repos/JestonBlu/Neighbor-Works/issues/comments/296803786","html_url":"https://github.com/JestonBlu/Neighbor-Works/issues/16#issuecomment-296803786","issue_url":"https://api.github.com/repos/JestonBlu/Neighbor-Works/issues/16","id":296803786,"user":{"login":"rmglazner","id":25467611,"avatar_url":"https://avatars0.githubusercontent.com/u/25467611?v=3","gravatar_id":"","url":"https://api.github.com/users/rmglazner","html_url":"https://github.com/rmglazner","followers_url":"https://api.github.com/users/rmglazner/followers","following_url":"https://api.github.com/users/rmglazner/following{/other_user}","gists_url":"https://api.github.com/users/rmglazner/gists{/gist_id}","starred_url":"https://api.github.com/users/rmglazner/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/rmglazner/subscriptions","organizations_url":"https://api.github.com/users/rmglazner/orgs","repos_url":"https://api.github.com/users/rmglazner/repos","events_url":"https://api.github.com/users/rmglazner/events{/privacy}","received_events_url":"https://api.github.com/users/rmglazner/received_events","type":"User","site_admin":false},"created_at":"2017-04-24T19:53:14Z","updated_at":"2017-04-24T19:53:14Z","body":"That makes sense, thank you for explaining!"}}
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