• Nikita Sunita Mokal
• Saee Suresh Dalvi
• Sumedh Pundkar

: Email spam continues to be a pervasive issue, posing threats to user privacy, productivity, and security. Machine learning (ML) techniques have emerged as effective tools for automated spam detection, offering the potential to adapt to evolving spamming tactics. This study proposes a hybrid machine learning approach for email spam detection, leveraging the strengths of both Random Forest (RF) and Gradient Boosting (GB) algorithms. The hybrid model aims to enhance classification accuracy and robustness by combining the ensemble learning capabilities of RF with the boosting power of GB. The proposed framework involves feature extraction from email datasets, preprocessing, and model training using the hybrid RF-GB algorithm. Evaluation metrics such as accuracy, precision, recall, and F1-score are employed to assess the performance of the hybrid model against individual RF and GB classifiers. Experimental results demonstrate the effectiveness of the hybrid approach in achieving superior spam detection performance, thus offering a promising solution for combating email spam in real-world applications.

Machine Learning algorithm, Random Forest (RF), Gradient Boosting (GBT), Hybrid approach.

"Email Spam Detection Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.p350-p356, May-2024, Available :http://www.jetir.org/papers/JETIR2405G48.pdf

"Email Spam Detection Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppp350-p356, May-2024, Available at : http://www.jetir.org/papers/JETIR2405G48.pdf