An ML-Powered Framework for Email Spam Identification
Keywords:
Ubiquitous, Phishing, Machine Learning, Spam, PredictionsAbstract
Email remains a globally ubiquitous communication tool due to its ease of use and speed. However, its effectiveness is often compromised by an inability to accurately filter unwanted messages. A growing number of reported cases involve the theft of personal information or phishing attempts conducted via email. This project explores the application of Machine Learning (ML) to enhance spam detection. ML, a facet of artificial intelligence, enables systems to automatically learn and improve from data without explicit programming. A binary classifier will be employed to categorize email content into "spam" or "ham" (legitimate mail), aiming for more accurate predictions. The primary objective of this model is to detect and classify words both rapidly and precisely.
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