Email Spam Detector - Machine Learning Model (Dockerized)

This project is a Machine Learning-based Spam Detector that classifies messages as spam or not spam using a trained Naive Bayes model. The model is built using scikit-learn and is packaged inside a Docker container for easy deployment and usage.

Features

• Trains a Naive Bayes model using a labeled dataset of SMS messages.
• Uses TF-IDF vectorization to transform text into numerical features.
• Saves the trained model and vectorizer for future predictions.
• Provides a Dockerized environment for easy execution.
• Allows spam detection for new messages via a command-line interface.

Dataset

The model is trained on a publicly available SMS spam dataset: https://raw.githubusercontent.com/justmarkham/pycon-2016-tutorial/master/data/sms.tsv

Project Structure

📂 email_spam_detection ├── Dockerfile ├── email_spam_detector.py # Script to train and save the model ├── predict.py # Script to predict if a message is spam ├── requirements.txt # Python dependencies ├── spam_detector_model.pkl # Trained model (saved) ├── spam_detector_vectorizer.pkl # TF-IDF vectorizer (saved) └── README.md

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Setup & Installation

To get started, you'll first need to clone the repository. Then, you can choose to run the project using Docker or locally with Python.

1. Clone the repository:

git clone [email protected]:sempedia/email_spam_detector.git
### Running with Docker
Ensure you have **Docker** installed on your machine. Then, follow these steps:

1. **Build the Docker Image:**
```sh
docker build -t spam-detection .

2. Run the Model Training Script:

docker run --rm spam-detection

3. Run a Prediction on a Sample Message:

docker run --rm spam-detection python predict.py "Win a free iPhone now!"

Usage

Training the Model

The email_spam_detector.py script downloads the dataset, trains the model, and saves it for future use.

To manually run it (without Docker):

python email_spam_detector.py

Predicting Spam

The predict.py script loads the trained model and classifies a given message as Spam or Not Spam.

Example usage:

python predict.py "Congratulations! You've won a free vacation!"

Dependencies

This project requires:

• Python 3.9+

• pandas

• scikit-learn

• joblib

• scipy

• numpy

• The requirements.txt file is included in the project structure for easy installation. If running locally, install dependencies using:

pip install -r requirements.txt

License

This project is open-source and available for use under the MIT License.

🚀Author:

Alina Bazavan (sempedia GitHub profile: https://github.com/sempedia)

Happy coding! 🚀

Contributing

Contributions are welcome! If you have suggestions for improvements or find bugs, please open an issue or submit a pull request.