🔍 Phishing and Spam Detection – Email Classification

A complete machine learning pipeline for classifying emails as Ham (legitimate), Spam, or Phishing using multiple datasets.
This project combines data preprocessing, TF-IDF vectorization, SMOTE oversampling, and Random Forest classification, and provides a FastAPI backend for real-time predictions.

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📌 About

With the rise of phishing and spam emails, automated detection is crucial for cybersecurity.
This project:

• Loads and preprocesses multiple email datasets
• Converts email text into TF-IDF vectors
• Handles imbalanced classes using SMOTE
• Trains a Random Forest classifier
• Exposes the trained model through a FastAPI REST API

It is suitable for AI/ML practitioners, cybersecurity enthusiasts, and anyone interested in real-time email classification.

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🛠️ Features

• 📥 Combines datasets: SpamAssassin, CEAS_08, Nazario, Nigerian_Fraud
• 🧹 Cleans and merges subject & body of emails
• 📊 Converts text to numerical features with TF-IDF
• 🔁 Balances data with SMOTE
• 🤖 Trains a Random Forest classifier
• ⚡ Serves predictions through FastAPI endpoints
• 📈 Generates confusion matrix and classification metrics

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📁 Project Structure

phishing_spam_api/
│
├── app.py                # FastAPI application
├── model/
│   ├── model.pkl         # Trained model (generated via notebook)
│   ├── vectorizer.pkl    # TF-IDF Vectorizer (generated via notebook)
│   └── train_model.py    # Script to train and save model/vectorizer
├── data/                 # Original CSV datasets
├── phishing-and-spam-detection.ipynb # Notebook for training & downloading artifacts
├── requirements.txt
└── README.md

⚙️ Installation & Setup

git clone https://github.com/Bilal-73/Phishing-and-Spam-Detection.git
cd Phishing-and-Spam-Detection

🧪 Training the Model & Downloading Artifacts

The trained model and vectorizer are not included due to file size. You can generate them using the Jupyter Notebook

👤 Author

Bilal Imran

• 💼 AI / ML & Full-Stack Enthusiast
• 🔗 GitHub: https://github.com/Bilal-73

⭐ Show Your Support If you found this project useful:

• ⭐ Star the repository
• 🍴 Fork it
• 💡 Suggest improvements