Spam SMS Classifier using Machine Learning Algorithms

Mobile SMS communication is insecure as a result of a significant problem with spam detection. A technique or model with high accuracy and precision is required to address this spam SMS issue. The amount of spam emails has dramatically increased over the last few years. SMS spam has major negative impacts since it harms both consumers and service providers, eroding their mutual trust to a great extent. Different types of classifier algorithm have been implemented like Naïve bayes, Random Forest, KNN and Support vector classifier on a raw dataset collected from UCI repository in this research. Metrices like Accuracy, Precision and Recall are takes as performance metrics for calculating the efficiency of the algorithm. After experimenting, the result of these algorithms and compared them with another models. We showed the comparison using Visualization Techniques.