“Predicting 911 Call Urgency in New York City” predicts the severity level of a 911 call and the time it would take emergency services to reach a given caller. The objective of this project is to gather valuable information about the performance of emergency services so dispatch operators could better allocate their resources for individual well-being in the future. The project uses EMS Dispatch Data from NYC OpenData, which includes features such as the severity level code, response time, and location information.
I love the team’s effort in making sure the data selected are generally applicable to the entire dataset. For example, the team chose data in 2019 and plotted the average response time grouped by zip code to ensure that 2019 is representative of all other years. Additionally, the team did a great job of reasoning the correct loss function and regularizer to use by identifying a large number of outliers and switching to l1 loss. Furthermore, I’m impressed by the application of AutoML pipeline. This showed that the team has spent lots of time understanding the class material and improving their model.
I think the team could have specified what features they decided to use instead of mentioning “some of the data columns we believed to be most relevant.” Additionally, even though encoding location information as one-hot vectors is certainly enough for the scope of the project, the team might use different methods as future steps. Lastly, even though the model choices are great, I would like to see more explanations on why linear approximation is emphasized in the report.
Overall, I think the report is well-written and the group has done a great work.
“Predicting 911 Call Urgency in New York City” predicts the severity level of a 911 call and the time it would take emergency services to reach a given caller. The objective of this project is to gather valuable information about the performance of emergency services so dispatch operators could better allocate their resources for individual well-being in the future. The project uses EMS Dispatch Data from NYC OpenData, which includes features such as the severity level code, response time, and location information.
I love the team’s effort in making sure the data selected are generally applicable to the entire dataset. For example, the team chose data in 2019 and plotted the average response time grouped by zip code to ensure that 2019 is representative of all other years. Additionally, the team did a great job of reasoning the correct loss function and regularizer to use by identifying a large number of outliers and switching to l1 loss. Furthermore, I’m impressed by the application of AutoML pipeline. This showed that the team has spent lots of time understanding the class material and improving their model.
I think the team could have specified what features they decided to use instead of mentioning “some of the data columns we believed to be most relevant.” Additionally, even though encoding location information as one-hot vectors is certainly enough for the scope of the project, the team might use different methods as future steps. Lastly, even though the model choices are great, I would like to see more explanations on why linear approximation is emphasized in the report.
Overall, I think the report is well-written and the group has done a great work.