Automatic Mail Deletion System Using Ml Algorithms

Email communication has become an essential part of modern digital communication; however, the rapid growth of unwanted emails such as spam, advertisements, and phishing messages creates difficulties in managing inboxes effectively. This project proposes an Automatic Mail Deletion System using Machine Learning algorithms to automatically identify and remove unwanted emails from the inbox. The system analyzes email content, subject lines, sender information, and other metadata to classify emails as important or unwanted. Machine learning algorithms such as Naïve Bayes, Support Vector Machine (SVM), and Random Forest are implemented to build the prediction model. The dataset consists of labeled email messages that are categorized as spam or non-spam. Data preprocessing techniques such as text cleaning, tokenization, and feature extraction are applied to prepare the dataset for model training. The proposed system improves email management by automatically filtering and deleting irrelevant emails, reducing manual effort, and enhancing user productivity and security in digital communication environments.