Predictive Analytics Based on AutoML Email Spam Detection

The dissemination of digital content through unsolicited, mass distribution is known as “spamming,” with email being a common method of transmission, sending unwanted messages that cybercriminals can utilize to trick victims and get confidential credentials from the casualty. Spammers use many forms of communication to bulk-send their unwanted messages. Some of these are marketing messages peddling unsolicited goods. Other types of spam messages can spread malware, scam messages divulging personal information, or scary messages. Spam can exist for many reasons, but it can be used for malicious purposes such as Passwords and other sensitive data about intended Users. To overcome the security breach, the classification of spam emails for comprehending spam has been done using a variety of techniques, such as machine learning and natural language processing. This paper proposes a novel technique for email spam detection, the spam email is classified for the understanding of spam has been done using the H2O which is the name of the entire machine learning platform developed by H2O.ai, that includes the neural network algorithm as one of many algorithms available for building predictive models. The focus of the experiments is on email messages dataset. The data were divided into a training group and a test group, where the results showed that our approach outperformed.