SpamGuard Pro

SpamGuard Pro is a sophisticated spam detection prototype leveraging machine learning techniques with scikit-learn and Python. This project serves as a foundational reference for those interested in developing advanced spam detection systems.

Screenshot

Project Overview

The project comprises two primary Python scripts:

1. load_dataset.py

   • Initializes the project by loading the SMS Spam Collection dataset into a pandas DataFrame.
   • Dataset: SMS Spam Collection Dataset
   • Location: ./datasets/SMSSpamCollection

2. spam_detection_model.py

   • Implements a robust spam detection model utilizing the Multinomial Naive Bayes classifier.
   • Employs the CountVectorizer from scikit-learn for feature extraction, transforming text messages into a numerical format.
   • Trains the model on a training subset (X_train and y_train) and evaluates its performance on a test subset (X_test).
   • Presents accuracy, confusion matrix, and classification report as key metrics.

How to Run

1. Open a terminal or command prompt.
2. Navigate to the project's root directory.
3. Execute the following commands:
   • python load_dataset.py
   • python spam_detection_model.py

Dependencies

• Python 3.x
• pandas
• scikit-learn

Install dependencies using:

pip install pandas scikit-learn

Machine Learning Technology

This project harnesses machine learning technology for spam detection:

• Feature Extraction:

• Utilizes the CountVectorizer to convert text messages into a numerical format, creating a matrix of word counts.

• Machine Learning Model: Employs the Multinomial Naive Bayes classifier, a powerful algorithm for text classification tasks.

• Training and Prediction: Trains the model on a subset of the SMS Spam Collection dataset and evaluates its performance on a separate subset.

• Evaluation Metrics: Presents accuracy, confusion matrix, and classification report as essential metrics for assessing model performance.

License

This project is shared under the GNU General Public License v3.0.

Note

• This project is designed as a foundational reference and is not intended for production use. It provides a starting point for the development of more advanced spam detection systems.

• Feel free to explore, modify, and use the code according to the terms of the GPL-3.0 license.