📧 Email Spam Detection Web App

A Machine Learning project that detects whether an email message is Spam or Not Spam. The project includes a trained model and a Streamlit web application where users can input messages and test different ML models.

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🚀 Features

• Spam detection using Machine Learning

• Three classification models:

  • Logistic Regression
  • Naive Bayes
  • Support Vector Machine (SVM)

• Text preprocessing and TF-IDF vectorization

• Interactive Streamlit web interface

• Model comparison capability

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🧠 Models Used

The following models were trained and evaluated:

Model	Description
Logistic Regression	Linear model commonly used for classification
Naive Bayes	Probabilistic classifier suitable for text data
Support Vector Machine	Powerful classifier for high-dimensional data

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📂 Project Structure

Email-Spam-Detection
│
├── datasets/
│   └── email.csv
│
├── EmailSpamDetection.py   # Training script
├── app.py                  # Streamlit web app
│
├── vectorizer.pkl          # TF-IDF vectorizer
├── logistic_model.pkl
├── nb_model.pkl
└── svm_model.pkl

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⚙️ Installation

Clone the repository:

git clone https://github.com/yourusername/email-spam-detection.git
cd email-spam-detection

Install dependencies:

pip install -r requirements.txt

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▶️ Run the Web App

streamlit run app.py

Then open the browser and test messages with different models.

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💡 Example Test Messages

Spam example:

Congratulations! You have won a free prize. Claim your reward now!

Normal message:

Hey, are we still meeting for lunch today?

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🛠 Technologies Used

• Python
• scikit-learn
• pandas
• Streamlit
• TF-IDF Vectorization

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📌 Future Improvements

• Better NLP preprocessing
• Model probability scores
• Visualization of model comparison
• Deployment of the web app

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👨‍💻 Author

Devam Trivedi AI & Machine Learning Student