Peer-review gt332

[gtrebilcock]

NYCBuildingPermit Midterm Review

The project is about NYC (now converted to SF) building permits and the likelihood of getting approval given data about the applications region. The group is considering data from several SF government resources including data on: DOB permit issuance, subway entrances, crime rates, wifi spots and taxi travel. The groups objective is to identify factors that impact building permit approval, potentially providing recommendations to home and business owners.

One thing I like about this project so far is the diverse feature transformations the group has utilized in order to model future building permits. The group has considered many of the transformations we learned in the airBnB homework which have turned out to be very useful in future predictions. Another aspect of the project that I liked how the group dealt with selection bias which could have greatly influenced the models prediction accuracy. Finally, I like that the group considered more than one model for presentation as it helps the reader better follow the reasoning of the researchers and how they were trying to best approach modeling the given dataset.

One area for improvement on the project would be the description and reasoning for the explanatory variables considered in the model. This helps the reader align their thoughts with how the researchers are approaching their model. Another area for improvement would be a more detailed explanation for why the group used MSE as an error metric versus other possible error metrics that penalize outliers differently. One last area (which is very subjective to the reader) would include a reason that this study is important. Maybe even just a figure or estimate like $Xbn a year is spent on attempting to get building permits which gives the reader a sense for the implications of a good predicting model for this question.

Overall I enjoyed reading the report and good luck on the rest of the project.