Spam Mail Prediction using Machine Learning

Email communication is widely used for information exchange, but the increasing volume of spam emails poses serious security and productivity challenges. Traditional rule-based spam filtering techniques are ineffective against evolving spam patterns, making intelligent solutions necessary. This project presents a Spam Mail Prediction System using Machine Learning, where supervised learning algorithms are applied to classify emails as spam or non-spam based on their content. Natural Language Processing techniques such as tokenization, stop-word removal, and stemming are used for data preprocessing, while TF-IDF is employed for feature extraction. Machine learning models including Naive Bayes, Logistic Regression, and Support Vector Machine (SVM) are trained and evaluated using performance metrics like accuracy, precision, recall, and F1-score. The results demonstrate that machine learning-based models provide effective and adaptive spam detection with reduced false positives, making the proposed system suitable for real-world email filtering applications and future enhancements in cybersecurity and intelligent communication systems.