A Comparative Analysis of Selected Machine Learning Technique for Spam Email Detection

Abstract

This study explores comparative analysis of selected machine language techniques for spam email detection system. The proposed solution aims to improve user experience by accurately identifying and filtering out unwanted and malicious emails, thereby reducing clutter. Our experiments show high accuracy and efficiency in detecting spam emails, highlighting the system’s potential to significantly mitigate email-based cyber threats. This research contributes to ongoing cybersecurity efforts, offering a robust tool for safer and more efficient email communication. The project assessed three machine learning algorithms: Naïve Bayes, SVM, and KNN. The system was designed and implemented using the best-performing algorithm to detect unwanted and malicious emails. Performance metrics such as accuracy, precision, recall, and F1-score were used to evaluate the algorithms. These metrics were derived from a confusion matrix generated by the algorithms, trained using a dataset from Kaggle. The spam email detection system’s interface was designed using the Flask framework, a popular Python web framework. The machine learning models powering the system’s threat detection capabilities were trained using Jupyter Notebook, an interactive computing environment within the Anaconda Navigator platform. Flask’s flexibility and modularity allowed the development of a user-friendly web-based interface for interacting with the spam detection capabilities. Jupyter Notebook enabled the research team to experiment with various natural language processing and machine learning techniques, leading to the development of the comprehensive threat detection algorithms employed by the system. Combining Flask for the web interface and Jupyter Notebook for model training and development allows the spam email detection system to offer real-time email analysis, comprehensive threat detection, and customizable settings. This protects users from the evolving landscape of online scams and malicious activities.