Machine Learning Using Classification: A Comparative Study of Supervised Learning Algorithms with Application to Benchmark Datasets

This paper presents a comparative study of supervised machine learning classification algorithms including Logistic Regression, Decision Tree, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). The study is conducted on benchmark datasets such as the Iris dataset and the Breast Cancer dataset. The performance of each algorithm is evaluated using accuracy, precision, recall, and F1-score. The results show that SVM achieves the highest accuracy, while Decision Tree provides better interpretability. This research helps beginners and MCA students understand the strengths and limitations of different classification algorithms.