The New York City Department of Health and Mental Hygiene is responsible for regulating 24,000 restaurants. At least once per year, the Department conducts inspections randomly, with a holistic approach (i.e. food handling, food temperature, personal hygiene, and vermin control).The Department’s main goal is to achieve high quality standards in the establishments.
The purpose of the analysis is to enhance DOH analytical capabilities by pinpointing trends in NYC's inspection outcomes, crafting a segmented model approach that informs the Department with an interactive dashboard.
Our analysis aims to better inform inspectors what to look for, optimizing the inspection appointment and creating more targeted inspections. Overall, it will be a more efficient system for DOH inspections, acting as a supplementary tool to create an ecosystem of more sanitary conditions in NYC restaurants.
| Deliverable Type | Designed to be: | File Name |
|---|---|---|
| Raw Data | Given by NYC Dataset | NYC_rest_inspection.csv |
| Agreggated data | Code's output | Output_NYC_Aggregated.csv |
| Presentation | Implemented (in .pptx) | NYC-DOH-Capstone-Deck.pptx |
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- How to run the program
- Download the "Deloitte_GWSB_NYC_Segmentation.ipynb" file, and run on Google Colab, VSCode, Jupyter, or any platform that supports Python.
- Load the original dataset from the open-source NYC DOH website. In the program, the dataset is named "NYC_rest_inspection.csv."
- Load the packages necessary, and execute the program.
Any advise for common problems or issues.
Contact Authors:
Nada Mashkour: [email protected]
Emma Hudson: [email protected]
Manuel Chavez: [email protected]
Arnav Chaudhari: [email protected]
This project is an open public project.
|https://github.com/SelfExplainML/PiML-Toolbox | https://opendata.cityofnewyork.us/|