Comparative Analysis of Different Machine Learning Algorithms on Different Datasets
G. S Bapi Raju, C Manasa, Nandikatti Durga Bhavani, Jadi Amulya, Dangatla Shirisha · 2023
Machine Learning is used to train models and machines without the help of any human interventions and guides. Here the models and machines are trained using algorithms. As it is difficult to train the algorithms one by one and get the analysis of the best algorithm. This research study intends to compare different algorithms used in different datasets to know the best algorithm according to different domains and the overall best algorithm. In this study, most popular supervised algorithms like SVM, Decision Tree, Random Forest, KNN, Logistic Regression, XGBoost, Adaboost, Voting Classifier (Decision Tree + Random Forest), Bagging Classifier (KNN + SVM) and Naïve Bayes. This research study has chosen three different domains for datasets. Different datasets were used to check the efficiency of algorithms. Comparative analysis of the classifiers shows that all algorithms outperform the existing methods with a high accuracy.