Natural Language Processing based classifier for identifying spam emails and SMS messages

this research explores the development of a sophisticated classifier for screening spam emails and communications using Natural Language Processing (NLP). In today's digital age, where electronic communications continue to pour in, discriminating between beneficial and dangerous content is becoming increasingly important. The classifier uses NLP to analyze the intricacies of language used in Email/SMS and finds spam patterns. The classifier is trained on a dataset of both spam and non-spam messages using machine learning techniques, allowing it to reliably distinguish and categorize emails and SMS. This implementation's highest result for the Naïve Bayes Classifier is Bernoulli 0.98 percent. The goal is to improve the user experience by limiting exposure to irrelevant or potentially harmful content while assuring the seamless delivery of real communications.