Email classification analysis using machine learning techniques

Purpose In this digital era, email is the most pervasive form of communication between people. Many users become a victim spam emails and their data have been exposed. Design/methodology/approach Researchers contribute to solving problem by focus on advanced machine learning algorithms improved models for detecting but there still gap in features. To achieve good results, features also play an important role. evaluate performance applied classifiers, 10-fold cross-validation used. Findings The results approve that are correctly classified with accuracy 98.00% Support Vector Machine 98.06% Artificial Neural Network as compared other classifiers. Originality/value paper, Point-Biserial correlation each feature concerning class label University California Irvine (UCI) spambase dataset select best Extensive experiments conducted selected training different