Spam ham classifier.

The term "spam" refers to any form of undesired and unsolicited electronic communication that is distributed in mass. Email is the most common medium for the transmission of spam, although it can also be transmitted via text message, phone call, or through social media. Spammers send large numbers of unwanted messages using a variety of different modes of communication. Some of them are marketing communications trying to sell you products that you have not requested. Other sorts of spam communications have the potential to infect your computer with malware, deceive you into disclosing personal information, or scare you into thinking that you need to pay to get out of a sticky situation. The demand for more dependable and efficient anti-spam filters has significantly increased as a result of the rise in unwanted email messages, known as spam. In other hand "Ham" refer to the desired and solicited electronic communication. In this project we made use of various reliable methods such as the TFIDF vectorizer and classifiers such as svm, naive-bayes, random forest, and XGBoost, all of which contributed to our efficiency rate of 96%. Comparative analysis of the benefits and drawbacks of currently available machine learning algorithms as well as unanswered research problems pertaining to spam filtering are presented in this paper. Deep learning and deep adversarial learning are two potential future tactics that we mentioned as ways to effectively combat spam email. [ABSTRACT FROM AUTHOR]