Exploring Supervised Learning Methods for Predicting Cuisines from Their Ingredients - Yonathan Ferry Hendrawan; Omkar Chekuri (Vokasi UNESA Bulletin of Engineering, Technology and Applied Science, 2025)
This study explores the use of multi-class classification to predict cuisines based on ingredient list using a Kaggle dataset derived from the Yummly recipe database. The goal was to identify the most effective machine-learning techniques for classifying recipes into different cuisine regions based on their ingredients. Six supervised learning methods were examined: Backpropagation Neural Network, Support Vector Machine (SVM), Naive Bayes, Decision Tree, Random Forest, and AdaBoost. The preprocessing pipeline involvedtokenizing ingredients into numerical features, ensuring compatibility with machine-learning algorithms, and facilitating model training and evaluation. Among the models tested, the SVM and Random Forest algorithms performed the best, achieving accuracies of 76.7% and 73.2%, respectively. These results were relatively close to the top competition leaderboard accuracy of 83%. Our custom implementations oftheBackpropagation Neural Network and Decision Tree demonstrated competitive performance, though hardware limitations during experimentation prevented the full optimization of these models. The findings emphasize the critical role of factors such as parameter tuning, dataset size, and feature preprocessing in determining classification accuracy. Additionally, the study highlights how a combiningof well-selected algorithms and data preprocessing can yield meaningful improvements in prediction quality. All codesand materials used in this research are publicly available, enabling further exploration by other researchers and practitioners