Classification of Email Spam Detection using Python
Chalumuri Hari Harnadh · International Journal for Research in Applied Science and Engineering Technology · 2025
With the exponential growth of email usage, unsolicited spam emails have become a major concern, leading to productivity loss, bandwidth consumption, and serious security threats such as phishing and malware attacks. This paper presents a machine learning-based approach to effectively detect and filter spam emails. The proposed system leverages natural language processing (NLP) techniques to extract relevant features from email content and metadata. Various classification algorithms, including Naive Bayes, Support Vector Machines (SVM), and deep learning models such as Long Short-Term Memory (LSTM) networks, are evaluated for their performance in classifying emails as spam or ham. Experimental results on benchmark datasets, such as SpamAssassin and Enron, demonstrate high accuracy and low false positive rates, indicating the effectiveness of the proposed models. The implementation highlights the importance of intelligent filtering systems in enhancing email security and user experience.