Spam Message Classification using Machine Learning Algorithm

We use some communication means to convey messages digitally. Digital tools allow two or more persons to coordinate with each other. This communication can be textual, visual, audio, and written. Smart devices including cell phones are the major sources of communication these days. Intensive communication through SMSs is causing spamming as well. Unwanted text messages define as junk information that we received in gadgets. Most of the companies promote their products or services by sending spam texts which are unwelcome. In general, most of the time spam emails more in numbers than Actual messages. In this paper, we have used text classification techniques to define SMS and spam filtering in a short view, which segregates the messages accordingly. In this paper, we apply some classification methods along with “machine learning algorithms” to identify how many SMS are spam or not. For that reason, we compared different classified methods on dataset collection on which work done by using the Weka tool. We got 100% results from Random forest and random tree.

Keywords: Spam Messages, Classification, Spam Filtering, Comparison.