Detection Of Spam Messages In E-Messaging Platform Using Machine Learning

In recent years, the internet has surpassed print as the dominant source of knowledge Individuals who don’t have enough time to actually read the newspaper resort to social networking sites to keep up with current events. Nowadays, there is an increased no of spam messages sent by an unauthorized user to everyone. It will mislead the user into some malware attack and trick the user to steal some information. Spam communications contain dangerous links that can harm our system and access system data. Spam messages are the same as genuine professional messages and they are targeted at people who are not aware of these frauds. Thus, the Detection of spam messages in E-messaging platforms using machine learning models has become a must in the current system. Machine learning techniques are used to detect spam communications on E-messaging platforms like you tube, Messenger, and SMS. The SMS Spam dataset for Short Messaging Service gives an accuracy of 98%, YouTube-spam-collection-v1 for YouTube gives an accuracy of 92.10% and spam_ham_dataset for E-mail gives the accuracy of 94.20% is achieved respectively. Classification of spam messages varies on the different digital messaging platforms. As an outcome, we’ll use this to identify junk mail communications from numerous E-mail platforms in real-time. It also classifies the spam messages sent by bots. The Naive Bayes algorithm is used for classification.