Assessment of Enhanced Email Spam Detection System Through Machine Learning Algorithms

Over the last few decades, emails have emerged as the predominant mode of communication, with countless individuals and businesses relying on them for daily interactions and information exchange. However, amidst this reliance, the problem of spam emails has arisen. Spam emails, characterized as unsolicited commercial or unwanted messages, have become a significant challenge for email users. These emails are deliberately crafted to inconvenience recipients by consuming their time, computational resources, and potentially compromising their valuable data. The proliferation of spam emails has reached alarming levels, necessitating the implementation of an effective detection mechanism to identify and filter them. Machine learning techniques have proven to be highly efficient for classifying and detecting spam emails. This research focuses on evaluating the performance of three machine learning classification algorithms: Logistic Regression, Naïve Bayes Classifier, and Random Forest Classifier, in the context of spam email detection. The assessment is carried out using key metrics such as accuracy, precision, recall, and F1 score. By analyzing these metrics, the most proficient classification machine learning algorithm is determined, which can subsequently be utilized for the effective identification of spam emails.