🛡️ Phishing Detection Using Machine Learning

This project uses machine learning to detect phishing (scam) emails, helping to prevent email-based frauds.

It demonstrates how machine learning can be trained on email text data to classify whether an email is legitimate or phishing.

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📊 Dataset

This project uses a CSV dataset named phishing_dataset.csv.

Columns:

• email_text: The body of the email
• label: 1 for phishing, 0 for legitimate

Note: Ensure your dataset is placed inside the /data folder.

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🧠 Skills & Technologies Used

• Machine Learning:
  • Logistic Regression, Naive Bayes, Random Forest, etc.
• Libraries & Frameworks:
  • Scikit-learn for model training and evaluation
  • Pandas for data manipulation
  • NumPy for numerical computations
  • Matplotlib & Seaborn for data visualization
• Text Processing:
  • TF-IDF for text vectorization
  • NLTK or spaCy for natural language processing (optional)

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🧠 Model Details

• Text vectorization: TF-IDF
• Classifier: Logistic Regression
• Evaluation Metrics: Accuracy, confusion matrix, precision, recall

You can easily extend this by trying different ML models like:

• Naive Bayes
• Random Forest
• SVM

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🚧 Future Improvements

• Experiment with deep learning models (LSTM, BERT)
• Real-time email scanning via API or web app (Flask/Streamlit)
• Integration with Gmail API for real-time inbox monitoring
• Deployment with Docker or as a web service