EMAIL SPAM BEHAVIOUR SEIVING TECHNIQUE USING HYBRID ALGORITHM.

Email spam filtering is a critical task in today’s digital world, due to the significant increase in the volume of spam emails. To address this issue, various machine learning (ML) and statistical techniques have been proposed to classify spam and non-spam emails accurately. This paper proposes a hybrid algorithm that combines two techniques: the Naive Bayes (NB) algorithm and the Support Vector Machine (SVM) algorithm. The proposed algorithm first preprocesses the email data by removing stop words, stemming, and transforming the text data into a numerical format. Then, the NB algorithm is used to extract the relevant features from the email data. The extracted features are then fed into the SVM algorithm, which uses a non-linear kernel function to classify the emails as spam or non-spam. To evaluate the effectiveness of the proposed algorithm, it was tested on two standard email spam datasets: Enron-Spam and SpamAssassin. The experimental results show that the proposed hybrid algorithm outperforms the standalone NB and SVM algorithms in terms of accuracy, precision, recall, and F1-score. The hybrid algorithm achieves an accuracy of 99.7% and 98.6% on the Enron-Spam and SpamAssassin datasets, respectively, which are significantly better than the accuracy achieved by the NB and the SVM algorithms. In conclusion, the proposed hybrid algorithm is an effective approach for email spam filtering, and it can be us [ABSTRACT FROM AUTHOR]