Email Spam Detection – A Comparative Analysis of 4 Machine Learning Models

This article was published as a part of the Data Science Blogathon


This article aims to compare four different deep learning and machine learning algorithms to build a spam detector and evaluate their performances. The dataset we used was from a shuffled sample of email subjects and bodies containing both spam and ham emails in numerous proportions, which we converted into lemmas. Email Spam Detection is one of the most effective projects of Deep learning but this is often also one project where people lose their confidence to search out the simplest model for accuracy purposes. In this article, we are going to detect the spam in the mail using four different techniques and compare them to get the most accurate model.

Detecting Spam in Emails. Applying NLP and Deep Learning for Spam… | by Ramya Vidiyala | Towards Data Science



An email has become one of the foremost important kinds of communication. In 2014, there are




To finish reading, please visit source site