• KUKKALA TARAKA HRUSHIKESA SAI
• B. Prajna

Email spam has become a pervasive problem, leading to increased efforts in developing effective techniques for detecting and filtering out spam messages. Traditional rule-based and machine learning methods have shown promising results, but they often struggle to adapt to evolving spamming techniques. This project proposes a deep learning approach Bidirectional Encoder Representations from Transformers (BERT) for email spam detection that automatically learns and extracts relevant features from email data.

Email Spam detection, Machine, Learning, Deep Learning, Dataset

"EMAIL SPAM DETECTION USING MACHINE LEARNING AND DEEP LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.a808-a812, September-2023, Available :http://www.jetir.org/papers/JETIR2309097.pdf

"EMAIL SPAM DETECTION USING MACHINE LEARNING AND DEEP LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppa808-a812, September-2023, Available at : http://www.jetir.org/papers/JETIR2309097.pdf