SMS Spam Text Classifier

Overview

This project implements a machine learning-based SMS spam detection system. It classifies incoming text messages as Spam or Ham (Not Spam) using Natural Language Processing (NLP) techniques and supervised learning algorithms.

Features

• Preprocessing of SMS text (tokenization, stopword removal, stemming)
• Feature extraction using TF-IDF or Bag of Words
• Model training with algorithms like Naive Bayes, Logistic Regression, or SVM
• Evaluation metrics: Accuracy, Precision, Recall, F1-score
• Easy-to-use script for predictions on new messages

Project Structure

SMSSpamTextClassifier/
│
├── data/                # Dataset (e.g., SMS Spam Collection)
├── notebooks/           # Jupyter notebooks for EDA and model training
├── src/                 # Source code for preprocessing and model pipeline
├── models/              # Saved trained models
├── requirements.txt     # Python dependencies
└── README.md            # Project documentation

Installation

Clone the repository:

git clone https://github.com/Mulla6518/SMSSpamTextClassifier.git

Install dependencies:

python 3

Usage Train the model:

Predict a message:

python script.py --data spam.csv

License This project is licensed under the MIT License.