Machine Learning Model for the E-mail Spam Detection with Data Mining Techniques

Abstract

E-mail customers get several hundred spam messages on regular basis with a fresh content, from fresh addresses which are robotically produced by robot programming tool. The traditional methods namely dark-white lists are ineffective to filter the spam. The text mining (TM) methods are implemented to an e-mail for maximizing the yield the filtering of e-mail spam. The e-mail spam detection techniques have various phases such as to pre-process the data, extract the attributes, and classify the data. The pre-processing phase will clean the dataset, and features are extracted to identify the features having great impact on the target set. The combination of multiple classifiers is used in this phase for the classification focuses on integrating the SVM, NB, and Random Forest. Python is employed to execute the introduced system and diverse metrics such as accuracy, precision, and recall are considered for analyzing the outcomes. The results of proposed model show high improvement for the e-mail spam prediction.