Supervised Learning based E-mail/ SMS Spam Classifier

Abstract

Background: One of the challenging problems facing the modern Internet is spam, which can annoy individual customers and wreak financial havoc on businesses. Spam communications target customers without their permission and clog their mailboxes. They consume more time and organizational resources when checking for and deleting spam. Even though most web users openly dislike spam, enough are willing to accept lucrative deals that spam remains a real problem. While most web users are well aware of their hatred of spam, the fact that enough of them still click on commercial offers means spammers can still make money from them. While most customers know what to do, they need clear instructions on avoiding and deleting spam. No matter what you do to eliminate spam, you won't succeed. Filtering is the most straightforward and practical technique in spam-blocking strategies. Method: We present procedures for identifying emails as spam or ham based on text classification. Different methods of e-mail organization preprocessing are interrelated, for example, applying stop word exclusion, stemming, including reduction and highlight selection strategies to extract buzzwords from each quality, and finally, using unique classifiers to Quarantine messages as spam or ham. Results: The Nave Bayes classifier is a good choice. Some classifiers, such as Simple Logistic and Adaboost, perform well. However, the Support Vector Machine Classifier (SVC) outperforms it. Therefore, the SVC makes decisions based on each case's comparisons and perspectives. Conclusion: Many spam separation studies have focused on recent classifier-related challenges. Machine Learning (ML) for spam detection is an important area of modern research. Today, spam detection using ML is an important area of research. Examine the adequacy of the proposed work and recognize the application of multiple learning estimates to extract spam from emails. Similarly, estimates have also been scrutinized.