Machine Learning Based Email Spam Detection: Achieving High Accuracy and Efficiency

Abstract

Email communication has become an essential aspect of modern-day interactions, but the proliferation of spam emails poses significant challenges to users' productivity and security. This research paper presents a comprehensive study on the development and implementation of an efficient email spam detection and categorization system. The project aims to categorize emails into predefined sections by using the Support Vector Machine (SVM) model, Flask, and the Gmail API, ensuring accuracy and efficiency in email classification. The methodology involves data preparation, processing, storage, and management, ensuring robust security and privacy considerations. The system's three-tiered classification strategy enhances the accuracy of spam and ham detection. Future enhancements include integrating advanced machine learning models, user feedback mechanisms, and multi-platform support to adapt to evolving email trends and user preferences. This research contributes to the field of email management by offering a new approach to combat spam effectively and enhance email organization for users in the digital age.