🛡️ Phishing Email Detection App

Welcome to the Phishing Email Detection App! This web-based application is designed to help you identify whether an email is phishing or legitimate using a combination of powerful machine learning models, including state-of-the-art transformer-based approaches like BERT.

Built with Streamlit, this intuitive app allows users to input email text and receive real-time predictions, helping you stay safer online.

🚀 Demo

Experience the app in action:

👉 Click here to try the live app!

✨ Features

• 🔍 Real-time Prediction: Get instant classification for any email content you provide.
• 🤖 Multiple ML Models: Utilizes a diverse set of models for robust detection, including:
  • Logistic Regression
  • Naive Bayes
  • Support Vector Machine (SVM)
  • Random Forest
  • Gradient Boosting
  • BERT (Transformer-based Model)
• 📈 Comprehensive Analysis: Detailed model comparison and performance metrics are available in the included notebooks.
• ✅ User-Friendly Interface: An easy-to-navigate web interface powered by Streamlit ensures a smooth experience.

🧪 Tech Stack

This project leverages the following technologies:

• Python 3.10+
• Streamlit: For building the interactive web application.
• Scikit-learn: For classical machine learning models.
• Transformers (HuggingFace): For implementing BERT and other transformer models.
• PyTorch: The deep learning framework for transformer models.
• Pandas, NumPy: For data manipulation and numerical operations.
• Matplotlib, Seaborn: For data visualization.

📁 Project Structure

The repository is organized for clarity and maintainability:

Phishing-Email-Detecting/
├── app/
│ └── app.py # Streamlit web application UI
├── src/
│ ├── train_model.py # Script for training all machine learning models
│ ├── predict.py # Handles predictions for classic ML models
│ └── predict_transformer.py # Handles predictions for BERT-based models
├── models/ # Directory to store saved trained model files
├── notebooks/ # Jupyter notebooks for EDA and model performance comparison
├── data/ # Stores dataset CSVs used for training
├── requirements.txt # List of all project dependencies
└── README.md # This README file

⚙️ How to Run Locally

Follow these steps to set up and run the application on your local machine:

1. Clone the repository:
   git clone https://github.com/Venksaiabhishek/Phishing-Email-Detecting.git
   cd Phishing-Email-Detecting

2. ** (Optional) Set up a virtual environment (recommended):**
   python -m venv .venv
   # On macOS/Linux:
   source .venv/bin/activate
   # On Windows:
   .venv\Scripts\activate

3. Install dependencies:
   pip install -r requirements.txt

4. Run the Streamlit app:
   streamlit run app/app.py

   Your browser should automatically open the app.

📝 Sample Usage

Using the app is straightforward:

1. Paste your email content into the provided text box.
2. Click the "Predict" button.
3. The app will display whether the email is:
   • ✅ Legitimate
   • ❌ Phishing

📡 Deployment

This application is designed for easy deployment on Streamlit Cloud. To deploy your own instance:

1. Push your project to a GitHub repository (like this one!).
2. Log into your Streamlit Cloud account.
3. Link your GitHub repository, and Streamlit Cloud will handle the rest!

👨‍💻 Author

Abhishek P.V.S

Explore more of my work on my GitHub portfolio:
👉 Abhishek P.V.S's GitHub Portfolio

📄 License

This project is licensed under the MIT License. See the LICENSE file in the repository for full details.