Although breast cancer is rare in men, it is the most common variety of cancer in women and has the highest mortality rate. Early detection of cancer is critical for survival. Breast cancer can be effectively classified and predicted using machine learning methods. In this study, six machine learning algorithms were compared: SVM, MLP, DT, KNN, NB and LR. In addition, PCA, one of the feature reduction methods, was applied to the dataset and their performances were compared. The accuracy rate was used to evaluate the performance of the model and it was determined that the most successful model was logistic regression. The LR model achieved 98% accuracy. In addition, when PCA was applied, the SVM model achieved 98% accuracy, demonstrating high success in breast cancer prediction. These results emphasize the efficacy of machine learning techniques in the breast cancer diagnosis process.