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This project uses artificial intelligence and machine learning to predict missing ingredients in recipes. The project takes as input a dataset of orders from a restaurant or online store, and then trains a model to predict the missing ingredients based on the ingredients that are already present. For example, if an order contains 8 out of 10 ingredients for a poke bowl, the model can predict the missing 2 ingredients. The model used in this project is a random forest classifier. Random forest classifiers are a type of machine learning model that are known for their accuracy and robustness. The model is trained on a dataset of orders that have already been completed. The dataset includes information about the ingredients that were ordered, as well as the customer's rating of the meal. Once the model is trained, it can be used to predict the missing ingredients in new orders. The predictions are made using a process called inference. Inference is the process of using a trained model to make predictions about new data. In this case, the new data is the list of ingredients that are already present in the order. The predictions made by the model are displayed on a web page. The web page also includes information about the ingredients that were predicted, as well as the customer's rating of the meal. The customer can then accept or reject the predictions. This project has a number of potential benefits. First, it can help customers to save time when ordering food. Second, it can help customers to discover new ingredients that they might not have otherwise considered. Third, it can help restaurants to improve their customer service by providing customers with more accurate and personalized recommendations. This project is still under development, but it has the potential to revolutionize the way that people order food. The project is open source, and it is available for anyone to use or modify.