Classification of Engineering Journals Quartile using Various Supervised Learning Models

In scientific research, journals are among the primary sources of information. There quartiles or categories quality in which Q1, Q2, Q3, and Q4. These represent assessment journal. A classification machine learning algorithm is developed as a means categorization journals. The process classifying data to estimate an item class with unknown label called classification. Various algorithms, such K-Nearest Neighbor (KNN), Naïve Bayes, Support Vector Machine (SVM) employed this study, several situations for exchanging training testing data. Cross-validation Confusion Matrix values accuracy, precision, recall, error used analyzed performance. classifier finest accuracy rate KNN average 70%, Bayes at 60% SVM 40%. This research suggests assumption that algorithms article can approach SJR system.