Intelligent Email Spam Detection: A Machine Learning-Based Approach
Vinod Jain · 2025
Email spam emails frequently contain dangerous material like viruses, phishing attempts, and fraudulent schemes. Email spam detection is an essential component of cybersecurity. Machine learning models are required to increase accuracy and efficiency because traditional rule-based filtering strategies are insufficient to handle developing spam techniques. This study investigates how well five popular machine learning algorithms—Random Forest, Logistic Regression, Support Vector Machine (SVM), Decision Tree, and Multinomial Naïve Bayes (MultinomialNB) to detect and categorize spam emails. Different methods are used by each of these models to examine email content and detect spam. MultinomialNB is a probabilistic classifier founded on the Bayes theorem, excels at text classification problems. By merging several decision trees, Random Forest, an ensemble learning technique, improves generalization and decreases overfitting while increasing prediction accuracy. A linear model for binary classification called logistic regression uses learned weights for several features to calculate the likelihood that an email is spam. SVM, a potent classification method, finds the best hyperplane in a high-dimensional space to distinguish between spam and non-spam emails. A rule-based approach called a Decision Tree uses a hierarchical structure of decision nodes to classify emails, making it simple to use and analyze. A dataset of tagged spam and non-spam emails is used to assess the performance of these models. To ensure a thorough evaluation of the model, the dataset is divided into training and testing sets. To compare the efficacy of the models, evaluation criteria accuracy is employed. While all models perform well, the results show that SVM offer superior accuracy and achieved 98.45% accuracy. The results emphasize how crucial it is to choose the right machine learning models depending on the properties of the dataset, available computing power, and intended performance indicators.