COMPARATIVE STUDY OF SUPERVISED LEARNING ALGORITHMS

A key area of machine learning is supervised learning, in which models are trained with labelled data to forecast results. Six popular supervised learning algorithms— Naïve Bayes , Logistic Regression, k-Nearest Neighbours (k-NN), Decision Tree, Random Forest, and Support Vector Machine (SVM) are assessed in this paper. The Breast Cancer Wisconsin data collection was utilized to test the models. Objective to get high performance for Support vector machine algorithm. Output was assessed applying accuracy, and results were visualized using combined bar, line, and scatter plots. Random Forest attained the maximum accuracy, while simpler models provided interpretability and fast computation. This study offers insights for algorithm selection in practical applications.