Email Spam Filtering Technique from Various Perspectives Using Machine Learning Algorithms

The upsurge in the number of emails send for varying purposes and the dependency of life on them in some way or the other, has given rise to the detection of fraud email and also to the inventors of these emails. The work helps us to prove the efficiency in the detection of spams and prevents the users from opening such emails. The various perspective that are taken into consideration in the work includes parameter tuning, the division of data set on various test train numbers or ratio. Further, the formulation of the study related to parameters and test train set of problem, comparison by both the popular algorithm is discussed. This work has been done after preprocessing and feature extraction of the data. As many of these spam hampers our security, privacy, and often leads in stealing critical information so the categorization of these in an effective manner keeping into mind various parameters is of utmost importance.