Spam Email Detection System Using Artificial Intelligence and Natural Language Processing

Email remains a common channel for personal, educational, and business communication, but unsolicited messages and deceptive email campaigns create security and productivity concerns. This paper presents an AI- and natural-language-processing-based workflow for classifying email text as spam or legitimate (ham). The proposed pipeline validates labelled email data, normalizes text, converts messages into numerical features using Term Frequency–Inverse Document Frequency (TF-IDF), and applies a supervised classifier. Multinomial Naive Bayes is used as the baseline model, while Logistic Regression and a linear Support Vector Machine can be considered for comparison. The workflow separates dataset preparation, feature extraction, training, prediction, and evaluation so that the same fitted pipeline is used for new messages. Model quality is assessed using accuracy, precision, recall, F1-score, and a confusion matrix. The project report does not provide verified numerical evaluation results; therefore, performance values should be obtained by running the implementation on the selected labelled dataset and should not be assumed. The design provides a reproducible educational prototype that can be extended with URL analysis, multilingual processing, and email-client integration.