Email Spam Detection Using MachineLearning

Abstract

Email spam classification is a critical task in today's digital world, where the amount of spam 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 accurately identify 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 use NLP techniques
such as tokenization, stop word removal, stemming, and feature extraction to preprocess the text data and extract
relevant features.We will evaluate several ML algorithms such as Naive 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 measured using evaluation metrics such as
precision, recall, and F1-score. The project's outcomes will include a spam classifier model that can be integrated into an
email system to automatically filter spam emails, improving email security and productivity. Additionally, the project
will contribute to the advancement of NLP and ML techniques for email spam classification.