Cuisine Prediction Based on Ingredients

Abstract

Cuisine prediction based on ingredients is a complex task that has numerous applications in the food industry, including recipe recommendation and food delivery. Accurate and reliable prediction models have the potential to improve the efficiency and effectiveness of these and other applications. In this study, we sought to develop a prediction model for cuisine type based on ingredients using a large dataset called 1M Recipe Dataset and the machine learning technique of grid search with cross-validation. Our results showed that the model was able to achieve a mean cross-validated accuracy of approximately 73.7%. While this is a promising result, it is clear that there is still room for improvement and further research is needed to optimize the model and better understand the factors that influence cuisine prediction based on ingredients. Future research could focus on a range of approaches to improving the model, such as exploring different techniques for feature extraction and classification, incorporating additional data sources, and applying the model to other tasks and problems related to cuisine prediction. Additionally, further study is needed to understand the limitations of the model and how it can be extended or modified to better address the challenges of cuisine prediction. Overall, our study represents an important step forward in understanding the relationship between ingredients and cuisine, and has the potential to inform the development of improved prediction models and applications in a variety of contexts.