Spam Detection System Using Machine Learning
Spam Detection System
In today’s connected world, spam is a growing problem in online communication. From fake job listings and scam emails to misleading news and harmful SMS, spam shows up in many forms. The Spam Detection System is a professional machine learning project built to identify and filter out this kind of unwanted content.This advanced solution helps secure digital platforms by detecting spam across various channels. It’s perfect for use in larger systems that need reliable spam protection and control.
Overview
The Spam Detection System is a multi-layered tool designed to scan and classify different types of content—ranging from emails and text messages to voice notes and social media posts. It utilizes natural language processing and machine learning models trained to identify spam with high accuracy.
This system is crafted for developers, enterprises, and security enthusiasts aiming to implement intelligent spam filtering in their systems.
Technical Specifications
| Project Name | Language Used |
|---|---|
| Spam Detection System – Safeguarding Digital Spaces | Python |
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- Language/s Used: Python
- Python Version (Recommended): 3.x
- Type: Web Application / Machine Learning Based System
Available Features
- Email Spam Detection – Uses TF-IDF vectorization and pre-trained models to filter spam emails.
- SMS Spam Classifier – Recognizes spam messages using linguistic analysis.
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Voice Message Spam Classifier – Capable of identifying spam within voice messages using audio processing.
- Twitter Spam Analysis – Analyzes tweets to classify spam based on patterns and hashtags.
- Job Post Spam Detection – Validates job listings to identify fake or spammy job posts.
- News Spam Filter – Assesses news headlines or articles to detect fake or misleading information.
These features are backed by Jupyter Notebooks for model training and testing, and saved model files (.sav) for deployment.
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