This project applies machine learning algorithms - K-Nearest Neighbors (KNN) and Gaussian Naive Bayes (GNB) - to predict semiconductor manufacturing outcomes. Through feature selection and cross-validation, it identifies the most effective model and provides insights for future data-driven decision-making.