Abstract:
In practically every industry today, from business to education, emails are used. Ham and spam are the two subcategories of emails. Email spam, often known as junk email or unwelcome email, is a kind of email that can be used to hurt any user by sapping their time and computing resources and stealing important data. Spam email volume is rising quickly day by day. Today's email and IoT service providers face huge and massive challenges with spam identification and filtration. Email filtering is one of the most important and well-known methods among all the methods created for identifying and preventing spam. The amount of unwanted emails has increased due to the increased usage of social media globally, making the implementation of a reliable system to filter out such issues necessary. On the internet, spam emails are the most prevalent issue. Sending an email with spam messages is a straightforward process for spammers. Spammers are capable of stealing crucial data from our devices, including contacts and files. In recent years, numerous deep learning-based word embedding techniques have been created. This study provides an overview of various machine learning techniques (MLTs) for email spam filtering, including Naive Bayes, K-Nearest Neighbor, Logistic Regression, Gradient Boosting Classifier, and Random Forest. However, in this article, we classify, assess, and compare various email spam filtering systems and provide a summary of the overall situation with relation to the accuracy rate of various currently used methods. I got the best accuracy that was 98% with the help of The Random Forest Classifier algorithm.
