ZeroSpamAI

A Flask-based web application for detecting spam URLs and messages using a machine learning model. This project allows users to input a URL or message and receive a prediction on whether it is spam or not. It also provides analytics on model performance and feature importance.

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Features

• Spam URL/Message Prediction: Classify input as spam or not spam.
• Machine Learning Model: Random Forest classifier trained on lexical and domain-based features.
• Interactive Web Interface: User-friendly UI built with Flask, HTML, CSS, and JavaScript.
• Analytics Dashboard: View feature importance and model performance metrics.
• PDF Report Generation: Export prediction results and analytics to PDF.
• Domain WHOIS Lookup (Optional): Fetch domain age and registration details using python-whois.

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Project Structure

SPAM DETECTION/
├── app.py                   # Main Flask application
├── requirements.txt         # Python dependencies
├── data/                    # Sample datasets (CSV format)
├── models/
│   ├── best_model.pkl       # Pre-trained machine learning model
│   ├── feature_list.txt     # Ordered list of features used by the model
│   ├── feature_order.json   # JSON mapping feature names to column indices
│   └── feature_importance.png  # Visualization of feature importances
├── src/
│   └── phishing_detection.py  # Script for model training and evaluation
├── utils/
│   └── whois_utils.py       # Helper functions for WHOIS domain lookups
├── templates/               # HTML templates
│   ├── index.html           # Home page (prediction form)
│   ├── analytics.html       # Model analytics dashboard
│   └── pdf_template.html    # Layout for PDF report
├── static/
│   └── style.css            # Custom styles
└── Readme.md                # Project documentation

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Installation

1. Clone the repository

   git clone https://github.com/<your-username>/spam-detection.git
   cd spam-detection/SPAM\ DETECTION

2. Create a virtual environment (optional but recommended)

   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

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Usage

1. Run the Flask app

   python app.py

2. Open your browser and navigate to http://127.0.0.1:5000/.

3. Enter a URL or message in the input form and submit to see the prediction result.

4. View Analytics via the "Analytics" link in the navigation bar to explore feature importances and model metrics.

5. Generate PDF Report by clicking the "Download PDF" button on the analytics page.

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Model Training (Optional)

If you wish to retrain the model on your own dataset:

1. Prepare your dataset in data/ folder as a CSV with columns: text, label.

2. Adjust feature extraction and training parameters in src/phishing_detection.py.

3. Run training script:

   python src/phishing_detection.py

4. New model & artifacts will be saved to the models/ directory.

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