Spam emails have become a serious problem in modern digital communication, causing security threats such as phishing, fraud, and malware attacks. Manual filtering of emails is inefficient and unreliable due to the continuously evolving nature of spam. This project focuses on developing an automated spam email detection system using machine learning techniques. The proposed system analyzes the content of emails to classify them as spam or legitimate. Text preprocessing methods such as tokenization, stop-word removal, and stemming are applied to clean the email data. Feature extraction is performed using the Term Frequency–Inverse Document Frequency (TF-IDF) technique. A Naive Bayes classifier is employed to build the detection model due to its simplicity and effectiveness. The system is trained and tested on a publicly available dataset obtained from the UCI Machine Learning Repository. Experimental results demonstrate high classification accuracy and a low false-positive rate. The proposed approach is computationally efficient and suitable for real-time email filtering. This project highlights the importance of machine learning in enhancing email security. Future improvements may include deep learning models and adaptive spam filtering techniques.
Spam Email Detection, Machine Learning, Naive Bayes, Text Classification, TF-IDF
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