A Comprehensive Exploration of ML Algorithms for Spam and Ham Email Classification (SHEC)

Business communications face major security risks from spam mail thus organizations must identify between genuine ham emails and unwanted spam emails. The proper management of this problem serves as an essential measure to stop phishing attempts and malware propagation as well as protect workplace performance. Machine learning algorithms assume a critical function for automatic email spam and ham detection and classification. This research article demonstrates that machine learning technology provides valuable solutions for fighting spam emails since it enhances detection capabilities and decreases unneeded email alarms. Our survey analyzes multiple performance and reliability aspects among different algorithms especially how logistic regression functions. The research adds to the creation of improved security measures and efficiency standards for business email systems.