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This is a prototype of replicating Chicago's Food Inspection Forecasting project with DC data in python.
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README.md

DC Restaurant Violation Forecasting

This is a prototype of replicating Chicago's Food Inspection Forecasting project with DC data in python.

Overview

In 2014, the Department of Innovation and Technology for the city of Chicago built an algorithm to predict likely health code violations for restaurants based on publicly available data. They turned this into an open source project, freely available on github, so other cities could implement this model.

As of an early 2016 article published by The Atlantic only one other place in the country has taken advantage of Chicago's work to implement this model for their locality, Montgomery County, MD. With the assistance of Open Data Nation the model has been adapted to work in a different environment. Montgomery County is comprised of 500 square miles of urban, suburban, and rural territory.

Open Data Nation is expanding this experience to build a data product called FIVAR, a web application that uses real-time open data to make food inspection smarter.

This objective of this project is to replicate the process in Washington, DC using Python as a proof of concept project. This was undertaken as a Capstone project by Nicole Donnelly and Jonathan Boyle as part of the General Assembly Data Science Immersive This project has not been coordinated with the District of Columbia government.

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