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
Introduction Concerns over air pollution and the environmental problem of acid rain have made governments all over the world tighten their regulations
Roger Rumbu check
Huan Li, Elsayed Oraby, Jacques Eksteen
Waste printed circuit boards (WPCBs) are a complicated and valuable fraction of electric and electronic waste. The recycling of them is critical to av
Roger Rumbu
TohoKu University
This article reports the development of a hybrid polymeric solid electrolyte designed to enhance the safety and performance of lithium-ion batteries (