SPAM EMAIL DETECTION USING DIFFERENT CLASSIFIERS

Abstract

Due to the widespread use of social media, the amount of unwanted emails has increased, necessitating the implementation of a reliable system to filter them out. Spam emailsare now the most common issue on the internet, despite email beingone of the fastest and most cost-effective forms of communication. Over the past few years, there has been a significant increase in spam emails due to the growing number of email subscribers. This study employs four classifiers (Random Forest, XG Boost, Naïve Bayesian) to classify email data, with varying data and feature sizes. The final classification result is '1' if the email is spam and '0' if it is not. The study was conducted using Python and implemented in a Jupyter notebook. This technique can also be utilized in monitoring social media and brand activity. The paper presents a machine learning approach for identifying spam emails by detecting spam content within the message. With machine learning, computers can learn how to perform a task without being explicitly programmed. This method uses data to generate a program that performs a task, such as classification. Unlike knowledge engineering, machine learning techniques require pre-classified data to create a training dataset that is used to fit the learning algorithm in the machine learning studio. The objective of this study is to train, test, and compare various classifiers. The rest of the paper is organized as follows: Section 2 defines the researchers' contribution in this field. Section 3 describes the experimentation framework, dataset, procedures, andlibraries. Section 4 summarizes the findings, and Section 5 concludes the paper's potential for future research.