AI-Powered Scam Message Detector

An intelligent Python-based tool that identifies scam messages using a custom rule-based and machine learning model. Designed to analyze message patterns, assign scores to multiple scam indicators, and classify input as SCAM or REAL — all while explaining why.

Features

• Classifies input messages as SCAM or REAL
• Uses a custom-built feature extractor for:
  • Money requests
  • Urgency
  • Grammar issues
  • Suspicious links
  • Upfront payments
  • Celebrity references
  • Reward offers
  • Pressure tactics
  • Unusual contact methods
  • Official appearance fakes
  • Unsecured sources
  • Threats or blackmail
• Machine Learning classifier (Random Forest)
• Accuracy report with precision, recall, and F1-score

Dataset Structure

Your dataset file labeled_dataset.csv should look like this:

message,label,urgency,money_request,official_appearance,reward_offer,celebrity_reference,grammar_issues,unusual_contact_method,pressure_to_act,suspicious_link,upfront_payment
"Message text",scam,0.9,1.0,0.7,0.6,0.0,0.4,0.2,0.9,0.0,0.5

How to Run

1. Clone this repository

git clone https://github.com/Debottam1234567890/Scam_Detector.git
cd scam_detector

2. Set up your Python environment

Make sure you're using Python 3.12 or higher.

python3 -m venv venv
source venv/bin/activate  # On Mac/Linux
venv\Scripts\activate     # On Windows
pip install -r requirements.txt

If there's no requirements.txt, install dependencies manually:

pip install scikit-learn pandas

3. Run the program

python3 scam_detector.py

You will see a classification report and a prompt for entering messages.

Sample Usage (CLI)

Enter a message to check (or 'quit' to exit):
Congratulations! You've won $5,000! Claim now or lose it forever.

Prediction: SCAM

Enter a message to check (or 'quit' to exit):

Sample Usage (GUI)

python3 endpoints.py

You will see a website with the same scam detector

How It Works

Each message is processed by a feature extraction system that searches for 100+ scammy keywords across 12 different psychological and linguistic factors. These values are then used by a Random Forest model to classify the message.

License

This project is licensed under the MIT License — feel free to use, modify, and share.

Author

Debottam Ghosh
GitHub: @Debottam1234567890

🤝 Contributing

Contributions are welcome! To contribute:

1. Fork the repo
2. Create your feature branch: git checkout -b feature/new-keywords
3. Commit your changes: git commit -m 'Add new keywords'
4. Push to the branch: git push origin feature/new-keywords
5. Open a pull request

📫 Contact

For questions or suggestions, feel free to open an issue or contact via GitHub.