FoodSafetyExplorer

Data dashboard for exploring food safety risks in the supply chain.

Summary

Analysis of historical and emerging hazards is critical to the development of an effective food safety system. Multiple regulatory agencies makes it difficult to stay aware of these hazards when sourcing from a global supply chain. A food safety professional can be diligent by checking multiple sources, or paying for a service to aggregate this information. But this approach is either time-consuming or expensive. Further, both approaches do not provide solutions in synthesizing this information for hazard analysis.

Data analytics and machine learning techniques can provide a solution to assist a food safety professional. In this project, I will create a solution that:

1. Collects recall data by API and/or web scraping from multiple sources.
2. Has a data pipeline that can be scaled for the addition of other sources.
3. Classify recalls using ML, NLP and text mining.
4. Utilize machine learning techniques for trending of emerging hazards.
5. Provides a data dashboard app for food safety professionals to explore historical recall information and emerging food safety hazards.

The Data

openFDA Recall Enforcement Endpoint

This endpoint includes food recall information from 2004-present.

openFDA Adverse Events Endpoint

This endpoint includes adverse health event and product complaint reports.

The Dashboard

{desciption pending}

Recall Classification

{desciption pending}

Emerging Trends

{description pending}

Data Backend

I used a factory pattern of builder classes to perform collection and processing of data into a PostgreSQL database.