A Review of SMS Spam Detection Using Features Selection

Now a days social media is very popular medium for communication. People used various strategies to communicate with others. Like email, send message etc. email is very costly medium for communication with others. So now a days SMS is the best and effective medium for communication. Because it is very easy to use. But SMS Spanning problem is increase day by day. Because people send some illegal message which is very inconvenient to the users. In this paper we tried to build a model of detection of the spam message using classification algorithm along -with feature selection technique. In this paper we have used Naïve Bayes, Support vector machine, Random Forest Classifier, and K-Neighbours Classifier. With the help of these technique, we selected better features that provided better accuracy. We removed the inappropriate and redundant attributes that are not valuable for the accuracy of the model. A comparative study of different algorithm that has been discussed in literature review is also compared in terms of Precision, Recall, F1Score, and accuracy.