SMS Spam Detection using Machine Learning

SMS spam detection using Naive Bayes algorithm is a widely used technique in the field of text classification. The main aim of this approach is to classify the incoming messages into spam or ham categories. The Naive Bayes algorithm works by calculating the probability of a message belonging to a particular class, based on the occurrence of different words in the message. In this paper, we present an efficient and accurate approach for SMS spam detection using the Naive Bayes algorithm. The proposed approach utilizes a pre- processing step for feature extraction, which includes tokenization, stop-word removal, and stemming. The Naive Bayes algorithm is then trained on a dataset of labeled messages to learn the probability distributions of different words in spam and ham messages. Finally, the trained model is used to classify incoming messages into spam or ham categories. The results of our experiments show that the proposed approach achieves high accuracy in detecting SMS spam messages. Keywords: Naive Bayes Algorithm, Text Classification, Probability, Feature Extraction, Tokenization, StopWord Removal, Stemming, Labeled Dataset, Probability Distribution, Accuracy