Using Supervised Machine Learning to Classify Customer Input Adrianna Steers-Smith: Statistician at Food Safety Inspection Service (FSIS) Background The Food Safety and Inspection Service (FSIS) is the public health agency in the U.S. Department of Agriculture responsible for ensuring that the nation's commercial supply of meat, poultry, and egg products is safe, wholesome, and correctly labeled and packaged. FSIS receives more than 50,000 policy related food safety questions from consumers, the food production industry, and Agency’s inspection staff through askFSIS. Methods Model: Multilayer Perceptron askFSIS AskFSIS is a resource available to the public that provides access to FSIS policy knowledge through Frequently Asked Questions and a portal that allows direct e-mail communication with a FSIS policy subject matter expert. Every e-mail to FSIS, and response from FSIS is saved within the application. The consumer routes their question to the subject expert (SME) by selecting a topic. Topics are assigned to each policy staff. Parameters: Bag of Words Tokenizer Gradient Decent Optimizer Sigmoid Function Activation Discussion/Results While there are other models to use for this type of classification problem using Bag of Words to Tokenize, sigmoid function for activation and gradient descent (a=0.01) for optimization with one hidden layer (20 nodes) produced a model that can with 82% accuracy, not much less than our intern, classify our complex data. For future studies we are exploring unsupervised clustering for training sets and expanding our classification problem to include all askFSIS data. ANN was used in this analysis, but we are exploring the value of LSTM for type of data. Purpose of Work Classifying and Analyzing trends in askFSIS data will refine our existing trend analysis process by reducing subjectivity and manual effort. This will allow FSIS to more rapidly clarify and improve policy guidance. Rapid responses will improve customer service and potentially reduce foodborne illness.