Skip to content

This project aims to design and develop a robust spam detection system that can accurately classify incoming messages or emails as spam or legitimate. Spam Detection Project Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

Notifications You must be signed in to change notification settings

Projects-Developer/Spam-Detection-Project

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

3 Commits
 
 

Repository files navigation

Spam-Detection-Project

Spam Detection Project Code, Document And Video Tutorial

SPam Classifier'

Abstract

This project presents a machine learning-based spam detection system designed to classify incoming messages or emails as spam or legitimate. The system utilizes natural language processing techniques and feature extraction to analyze message content and identify patterns indicative of spam. The proposed system achieves high accuracy and efficiency in detecting spam messages, providing a robust solution for email security and improving user experience.

Keywords:Spam detection, machine learning, natural language processing, feature extraction, email security, text classification, supervised learning, cybersecurity, artificial intelligence

Project include:

  1. Synopsis

  2. PPT

  3. Research Paper

  4. Code

  5. Explanation video

  6. Documents

  7. Report

Need Code, Documents & Explanation video ?

How to Reach me :

WhatsApp: +91 9310631437 (Helping 24*7) CHAT

Contact me for any kind of help on projects.

1000 Computer Science Projects : https://www.computer-science-project.in/

Mail/Message me for Projects Help 🙏🏻

About

This project aims to design and develop a robust spam detection system that can accurately classify incoming messages or emails as spam or legitimate. Spam Detection Project Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published