Employing Machine Learning Methods to Identify Spam Emails

Abstract

The issue of email spam has gotten significantly worse in recent years due to the rapid rise in internet users. Millions of unsolicited emails are sent every day worldwide, which presents problems for both individuals and businesses. These emails are frequently used for unethical and unlawful purposes, such as financial frauds, phishing, and the distribution of malicious malware. Spammers use the ease with which phony online profiles and email accounts can be created, making it difficult for recipients to Dangerous files or links that can install malicious software, steal personal information, or grant unauthorized access to a user's computer are frequently included in spam emails.

Older techniques of filtering emails based on basic principles are insufficient to stop these threats since spammers' methods of sending spam are constantly evolving. The necessity for more intelligent and automated systems that can accurately detect and prevent spam emails is demonstrated by this growing issue. Our project is using machine learning to develop a robust method for identifying spam emails in order to add rest his issue.

To distinguish between spam and legitimate emails, the system will consider both the content and additional information. We tested the system on a popular spam email dataset and discovered that the Random Forest algorithm performed the best. A machine learning technique called Random Forest mixes the output of numerous decision trees to produce predictions that are more trustworthy and accurate. Compared to employing a single decision tree, it is less likely to make mistakes and manages complex data better.