Spam Detection in Email using Machine Learning

In today's world, email is used in almost every industry, from business to education. Emails can be categorized into two categories: ham and spam. Junk emails, also known as spam messages, are emails that have been designed to harm recipients by wasting their time, computing resources, and stealing their valuable information. It is estimated that spam emails are increasing at a rapid rate. One of the most important and prominent spam prevention techniques is filtering email. Naive Bayes, Decision Trees, Neural Networks, and Random Forests are among the methods used for this purpose by researchers. In this project, I examine the Logistic Regression machine learning model for spam filtering in email by categorizing messages into appropriate groups. This study also compares the techniques based on accuracy, precision, recall, etc. The accuracy level for this project was around 97%. Towards the end, these insights and future research directions, and challenges are outlined.