Spam Detection System using Python

This repository contains code and resources for the project "Spam Detection System using Python". The goal of this project is to build a machine learning model that can accurately classify SMS messages as either 'spam' or 'ham' (not spam). Project Overview

Spam detection is a common application of Machine Learning in text classification tasks. In this project, we use the SMS Spam Collection dataset from the UCI Machine Learning Repository and train a Multinomial Naive Bayes classifier to detect spam messages.

Requirements

• Python 3.6 or above
• Natural Language Toolkit (NLTK)
• Scikit-learn

Usage

1. Clone this repository to your local machine.
2. Install the required libraries.
3. Download the SMS Spam Collection dataset from the UCI Machine Learning Repository and place it in the same directory as the Python script.
4. Run the Python script to train and evaluate the spam detection model.

Results

The performance of the model is evaluated using precision, recall, and F1-score. The results show that the model can accurately classify SMS messages as either 'spam' or 'ham'.

Future Work

This project serves as a starting point for spam detection. Future work could explore other techniques, use different machine learning models, or apply the model to other spam detection tasks such as email spam detection.