Spam Email Detection using Comparative Machine Learning
Anup Kumar Malakar, Mohammad Abu Yousuf · 2025
The increasing prevalence of spam emails poses a significant challenge to email users and service providers alike. Traditional rule-based and content-based spam filtering techniques are no longer effective against the sophisticated techniques used by spammers. Therefore, more advanced techniques are needed to improve the accuracy of spam detection. In this research, we propose a machine-comparative learning approach to detect spam emails. Our method involves using a range of classification algorithms, including Naïve Bayes, Random Forest and Support Vector Machine (SVM), to compare their performance in identifying spam and non-spam emails. The performance of the algorithms is evaluated based on various metrics, including precision, recall and F1-score. We use a dataset consisting of both spam and non-spam emails to train and test our models. The dataset is preprocessed to ex-tract relevant features, including the sender&s;s address, subject line and email content. We also use feature selection techniques to identify the most important features for classification our study demonstrates the effectiveness of machine comparative learning in spam email detection. The performance analysis of the spam email detection algorithm using comparative machine learning involves evaluating the accuracy, precision, recall, F1 score and AUC-ROC curve to determine the effectiveness of the algorithm in distinguishing between spam and legitimate emails. Our proposed method can be applied to improve the accuracy of spam filtering in email services, helping to reduce the impact of spam emails on users and service providers.