Email-SMS-Spam-Detection-Machine-Learning-powered-application

This is a Machine Learning–powered application that detects whether a given Email or SMS message is Spam or Not Spam (Ham). The project includes both the model training pipeline (Jupyter Notebook) and a Streamlit web app for real-time predictions.

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Features

• Preprocessing: Tokenization, Stopword Removal, Punctuation Removal, Stemming
• Feature Extraction: TF-IDF Vectorization
• Classification using Machine Learning models (final model saved as spam_model.pkl)
• Easy-to-use Streamlit Web App for instant spam detection
• Modular code with clear training (.ipynb) and deployment (.py) files

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Project Structure

├── app.py # Streamlit app for live spam detection
├── Email_Spam_Detection.ipynb # Jupyter Notebook (data preprocessing, training, evaluation)
├── spam_model.pkl # Saved trained ML model
├── vectorizer.pkl # Saved TF-IDF vectorizer
├── requirements.txt # Python dependencies
├── README.md # Project documentation

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Model Training

• Open Email_Spam_Detection.ipynb in Jupyter Notebook.
• The notebook covers: - Data Preprocessing - Feature Extraction (TF-IDF) - Model Training (Random Forest, Naïve Bayes, etc.) - Model Evaluation & Selection - Saving the trained model (spam_model.pkl) and vectorizer (vectorizer.pkl)

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Running the Streamlit App

Install requirements (recommended: create a new virtual environment):

pip install -r requirements.txt

Run the following command inside the project directory:

streamlit run app.py

License

This project is licensed under the MIT License — see the LICENSE file for details.