International Journal Of Imaging

Abstract

Email Spam Detection Using Machine Learning

Author: KADIYALA SHIVANI, KASIREDDY SRIVANI, SANDEEP, SINDHUJA, J.HEMALATHA

Abstract:

Email spam classification is a critical task in today's digital world, where the amount ofspam emails has increased dramatically. In this project, we propose to use machine learning (ML) and natural language processing (NLP) techniques to classify email messages as either spam or legitimate. The project aims to develop an efficient spam classifier that can accuratelyidentify and filter spam emails from legitimate ones. The dataset used in this project will consistof a large number of email messages with their corresponding labels (spam/ham). We will useNLP techniques such as tokenization, stop word removal, stemming, and feature extraction topreprocess the text data and extract relevant features.We will evaluate several ML algorithms such asNaive Bayes, Support Vector Machines (SVMs), and Random Forests to determine thebest model for spam classification. We will also perform hyper parameter tuning to optimize the model's performance. The accuracy of the classifier will be measu