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๐Ÿ›ก๏ธ 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.


๐Ÿ“Š 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.


๐Ÿง  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)

๐Ÿง  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

๐Ÿšง 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

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A machine learning model to detect phishing emails and prevent email scams.

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