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|In the era of Internet, Email is the best way for communication. Email spam is increasing day by day. Spam is the main reason for the financial loss on the internet, steal valuable information (bank account detail, password etc.), slow down internet bandwidth etc. Email spam is the mail that user does not want to receive. There are various existing algorithms for filtering the spam such as Naïve Bayes, Support vector machine, K-nearest neighbor and Decision tree etc. Email spam is also increasing day by day so existing algorithms will not be efficient in the future, hence, there is a need to combine two or more algorithms to enhance the performance of existing models using ensemble methods. Thus, various existing machine learning models have been explored and analyzed. The models have been evaluated based on various performance metrics. Further, the models have been compared with existing models to evaluate the accuracy.
|Spam dataset, Machine Learning Models, Validation
|Consensus Based Ensemble Model for Spam Detection
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