• Abhishek singh
• saloni prajapat
• Rahul kumar

It is a well-known fact that spam messages have emerged as a menace in the digital age and thus spam classification is imperative. Four sophisticated techniques to improve the accuracy of spam classification using ensemble techniques have been demonstrated in this study. In our strategy, an ensemble model is created by combining multiple algorithms such as Support Vector Machines, Random Forests, and Naive Bayes. Feature selection methods, including T F - I D F a n d w o r d embeddings also have been incorporated to source relevant features from the email contents. The ensemble model indicated effectiveness by performing better performance compared to the single classifiers with above 95% classification accuracy. Furthermore the findings emphasize the importance of ensemble techniques and open new avenues for more effective spam mitigation alongside the related issues of developing email filtering systems.

Spam Filtering, Machine Learning, Email Filtering, Word Embeddings, Precision, Recall, F1 Score, Cross-Validation, Data Pre-processing, Feature Engineering, Model Evaluation, Accuracy, Classification Accuracy

"A Brief Study and Analysis on Spam Detection Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.b628-b634, November-2024, Available :http://www.jetir.org/papers/JETIR2411180.pdf

"A Brief Study and Analysis on Spam Detection Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppb628-b634, November-2024, Available at : http://www.jetir.org/papers/JETIR2411180.pdf