Paper Key : IRJ************144
Author: Prof. Pramod T. Talole,Vaishanvi G. Sahane,Rakhi S. Zade,Poonam D. Kamble,Rohan D. Shingne
Date Published: 04 Apr 2024
Abstract
Email spam remains a persistent issue despite various attempts to mitigate its impact. In this paper, we propose an Email Spam Filtering System leveraging machine learning techniques. The system is designed to classify incoming emails as spam or non-spam (ham) using a trained model. We present the methodology, module description, and future scope of our system, highlighting its potential to enhance email security and productivity. Additionally, we provide insights into the implementation details and discuss the effectiveness of our approach in combating email spam. In this project, machine learning techniques are used to detect the spam message of a mail. Machine learning is where computers can learn to do something without the need to explicitly program them for the task. It uses data and produce a program to perform a task such as classification. Compared to knowledge engineering, machine learning techniques require messages that have been successfully pre-classified. The pre-classified messages make the training dataset which will be used to fit the learning algorithm to the model in machine learning studio.
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