An Analysis of SMS Spam Detection using Machine Learning Model

social-media is a very common medium for spammers to unethically overwhelm normal users with unsolicited or false content via social-networking. Now a days most of the people are aware of social media. In this paper, we have used SMS Spam Collection dataset that is taken from Kaggle. The Bow with TF and TF-IDF weighing schemes features are used for feature selection. And we used chi-square matrix for features selection. We have done the comparison of state of the art and proposed model. The results show that our proposed model Multinomial Naïve Bayes gave highest accuracy. We offer the variable status of each feature so that it is easy to abolish the inappropriate features. Our results illustrated that our proposed model accomplished effectively high detection rates in terms of high accuracy, compared with other considered researches