An Empirical Study of Classification Techniques by using Machine Learning Classifiers

In this transformational era, advancements in computational powers of machine learning applications, data classification task has put its roots from various engineering domains to an explosion of new strategies of data handling in the real-world applications scenario. Therefore, this study describes the implementation of eight classifiers (Logistic Regression, Support Vector Machines, Perceptron, Decision Tree, Random Forest, k-Nearest Neighbor, Gaussian Naïve Bayes and Linear Discriminant Analysis) on the iris dataset. Performance metrics like classification report and accuracy measures are evaluated on the iris dataset and it is observed experimentally that SVM classifier has given good accuracy measure of 99.1% over other classifiers.