Rakshak

Live Project Link :

https://spam-detection-tool-1.onrender.com

Youtube Link:

https://youtu.be/5uzirp4B6rI?si=CwZNA23-iHx6bHXB

Problem Statement:

Title: Develop Spam alert tool

Description: Develop a tool that can analyse and verify the source of any incoming call, link, SMS, mms and Email based on the inputs from the user. The solution should be able to check is source is genuine or spam

Solution : (Our Approach)

The Spam Detection and Prevention Tool named as Rakshak is a project developed during a hackathon aimed at detecting and preventing spam across various communication channels. This tool provides users with the ability to detect spam messages, emails, multimedia messages (MMS), and short message service (SMS). Additionally, it includes functionality to determine whether phone numbers are associated with spam activity. Furthermore, the project integrates a chatbot where users can seek advice on how to stay safe from spam and fraud.

Features:

Spam Detection:

Utilizes machine learning models to detect spam messages, emails, MMS, and SMS.

Phone Number Analysis:

Determines whether phone numbers are spam or legitimate.

Chatbot For Communication and awareness:

Includes a chatbot interface where users can inquire about spam prevention methods and safety tips.

Spam Call Prevention:

Utilizes Twilio API for spam call detection and prevention.

Tech Stack:

Flask:

Python web framework used for building the application.

Python:

Programming language utilized for backend development and machine learning model implementation.

Machine Learning Model:

Implements machine learning algorithms for spam detection.

APIs:

Integrates APIs for fetching data and enhancing spam detection capabilities.

Twilio:

Utilizes Twilio API for spam call detection and prevention.

Getting Started:

To get started with the Spam Detection and Prevention Tool, follow these steps:

1.) Clone the repository:

git clone https://github.com/Blacksujit/spam-detection-tool.git

2.) Install Dependencies:

pip install -r requirements.txt

3.) Run the Flask application:

python wsgi.py