E-MAIL Classifier Using NLP and Machine Learning
Sanskruti Jindal, Saurav Shishodia, Somya Garg, Tanisha Agarwal, Shashank Sahu · 2024
In today’s digital communication, email classification is an essential responsibility. Emails are arranged and classified according to their objective and content. Using artificial intelligence and machine learning, this project aims to develop an accurate email categorization system that can discriminate between emails categorized as spam and those that are not. It is planned that a training is performed on a wide range of content of email in order to identify the patterns that differentiate different message types.The evaluation of the performance is performed on accuracy and precision and contrasted with other email classification algorithms currently in use to demonstrate how effective and efficient it is. To determine which strategy is optimal, the accuracy of these approaches will be evaluated. To guarantee minimal preparation and optimize accuracy, a variety of algorithms, such as Naive Bayes, Random Forest, and XGBoost, will be used. Individual words are regarded as features in the Naive Bayes classification system. The objective of this project is to strengthen email management overall for users and to progress email classification systems.