SMS Spam Classifier

This project aims to classify SMS text messages as spam or ham (non-spam) using natural language processing (NLP) techniques and a machine learning model.

Introduction
Dataset
Installation
Usage
Model and Evaluation
Results
Contributing

Introduction

This project demonstrates a basic implementation of a spam text message classifier using a Naive Bayes model. The goal is to filter out spam messages from legitimate ones, thereby reducing unwanted messages.

Dataset

The dataset used for this project is a collection of SMS messages labeled as either 'spam' or 'ham'. The dataset is sourced from Kaggle and is available in a CSV file named spam-text-message-data.csv.

You can download the dataset from the following link: SMS Spam Collection Dataset on Kaggle

The dataset consists of two columns:

Category: Label indicating whether the message is 'spam' or 'ham'.
Message: The content of the SMS message.

Installation

To run this project, you need to have Python and the following libraries installed:

pandas
matplotlib
nltk
scikit-learn

You can install these dependencies using pip:

pip install pandas matplotlib nltk scikit-learn

Additionally, you need to download the NLTK stopwords:

import nltk
nltk.download('stopwords')

Usage

Clone this repository to your local machine :

git clone https://github.com/yourusername/spam-text-classification.git
cd spam-text-classification

Place the spam-text-message-data.csv file in the project directory.

Run the main.py script to execute the spam classification process:

python main.py

Model and Evaluation

The script performs the following steps:

Reads the dataset. Preprocesses the data by cleaning and stemming the text messages. Converts the text messages into TF-IDF features. Splits the dataset into training and testing sets. Trains a Naive Bayes classifier on the training set. Evaluates the model using accuracy, precision, and recall metrics.

Results

The confusion matrix and performance metrics (accuracy, precision, recall) are printed to the console after the model evaluation. Here is an example of the output:

Confusion Matrix:
[[955   0]
 [ 34 126]]

Accuracy: 0.9695
Precision: 1.0
Recall: 0.7875

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

Seyed-Alireza-Kargar/Spam-Text-Classification