🛡️ Phishing Email Detector

A machine learning-powered phishing detector that analyzes email content and identifies suspicious patterns, including malicious URLs.
Built with 💻 Python, 🧠 scikit-learn, and 🌐 Flask.

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🚀 Features

• ✅ Detects phishing vs legitimate emails
• 🔗 Extracts and analyzes suspicious URLs
• 🧠 Machine learning model (TF-IDF + Random Forest)
• 🌐 Web interface with light/dark mode toggle
• 🎨 Interactive, modern UI
• 🧰 Easy to retrain with your own dataset

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📸 Screenshot

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🏗️ File Structure

  phishing_detector/
  ├── app/
  │ ├── detector_api.py       # Flask web app
  │ └── templates/index.html  # Frontend
  ├── data/                   # Email datasets
  ├── models/                 # Model + vectorizer (generated after training)
  ├── scripts/
  │ ├── train_model.py        # Train model
  │ ├── predict.py            # Predict phishing or legit
  │ ├── preprocess.py         # Load datasets
  │ ├── feature_extractor.py  # TF-IDF vectorization
  │ └── url_analyzer.py       # Suspicious URL detection
  ├── requirements.txt
  └── README.md

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🧪 How to Run Locally

1. Clone the Repo

git clone https://github.com/hassanmajaro/phishing-email-detector.git
cd phishing-email-detector

2. Install Dependencies

pip install -r requirements.txt

3. Add Datasets

Place your email in the data/ folder:

• phishing_emails.csv
• legit_emails.csv

4. Train the Model

python scripts/train_model.py

5. Run the Web App

python app/detector_api.py

Open in your browser: http://127.0.0.1:5000

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📊 Sample Dataset Format

Both .csv files should have at least:

subject,body,from
"URGENT: Account Warning", "Click here: http://phish.com", "noreply@fakebank.com"

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🤝 Contributing

Pull requests are welcome!

If you have feature suggestions or want to help improve detection, feel free to open an issue or PR

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👨‍💻 Author

Majaro Hassan

Cybersecurity Enthusiast

https://github.com/hassanmajaro