Machine Learning based Hybrid Approach for Email Spam Detection
Chirag Bansal, Brahmaleen K. Sidhu
Abstract
Today, everyone uses official data via email. Spam is the number one subject on the Internet. Emails containing spam from spammers are easy to send. Spam fills up our inboxes with inappropriate emails. Spammers steal our confidential information from our devices, such as Files, contacts. Even if we have the latest technology, it is difficult to do it. This article aims to propose a Term Frequency inverse document frequency (TFIDF) method by implementing an artificial neural network. The effects are as compared in phrases of the confusion matrix, accuracy, and precision. To assess the suitability of ANN we got a tendency to use a Kaggle data set that has a less proportion of spammed emails and real emails. The Outputs show that the positive yield of ANN appears at the ratio of 97.5 8% by using TFIDF based ANN