Iman Youssif Ibrahim
Email is widely used for communication and file sharing, but also serves as a major channel for unsolicited and malicious content known as spam. This study aims to classify email messages as spam or non-spam using machine learning, and to compare the performance of several algorithms: logistic regression, random forest, Naive Bayes, decision tree, K-nearest neighbors (KNN), and support vector machine (SVM). A dataset of 5,172 emails with 3,002 features (including the 3,000 most frequent words) was used, split into 70% training and 30% testing. Each model was evaluated using Accuracy, Precision, Recall, F1-Score, and ROC AUC to determine its effectiveness in spam detection. Logistic regression achieved the best performance with 98% accuracy, followed closely by random forest with 97%. These results confirm that appropriately selected machine learning models can provide highly accurate spam filtering and enhance email security.
@article{7649b6d9-806e-45ed-9336-ad858d5cc80b,
title={Email Spam Classification Based on Logistics Regression },
author={Iman Youssif Ibrahim},
year={2025},
language={en}
}TY - JOUR TI - Email Spam Classification Based on Logistics Regression AU - Iman Youssif Ibrahim PY - 2025 LA - en ER -
Rajesh Jha, Bimal Kumar Jha
This book describes the application of artificial intelligence (AI) and machine learning (ML) concepts to develop predictive models that can be used t
Roger Rumbu
This paper provides a detailed investigation into the role of hot gas circulation in metallurgical fluid bed roasters, aiming to enhance heat and mass