Analyzing FDA Food Recall Patterns Using Machine Learning: Predicting Outcomes and Revealing Insights

Food is essential for human survival, and the global food industry plays a critical role in ensuring its availability. However, with this responsibility comes the need for rigorous regulation to ensure that harmful or contaminated food products do not reach consumers. Regulatory bodies like the U.S. Food and Drug Administration (FDA) must maintain strong oversight to prevent unsafe products from entering the market and when failures occur, to ensure that recalls are carried out swiftly and effectively.The objective of this project is to analyze FDA food recall data from 2017 to 2025, investigate the reasons behind recalls, identify patterns in terminated recalls, and develop predictive models to estimate the likelihood of a recall being terminated. The dataset, obtained from the official FDA website, includes recall dates, product descriptions, recall reasons, company names, and recall statuses.The project employs exploratory data analysis (EDA) to uncover patterns, clustering techniques to group similar recall reasons, forecasting methods to predict future recall trends, and machine learning classification to predict recall termination outcomes. Key findings reveal that the most common recall reasons include Listeria contamination, undeclared allergens, and other contamination issues. Additionally, company name and year were identified as the most influential predictors of recall termination.This work provides actionable insights for manufacturers and regulators, offering a data-driven foundation for improving food safety oversight, enhancing recall management, and ultimately protecting public health.