SPAM SMS Classification

This repository contains a Jupyter Notebook that implements a machine learning solution for classifying SMS messages as spam or non-spam. The project involves text preprocessing, feature extraction, and training a classification model.

Features

• Preprocessing: Removal of stopwords, punctuation, and normalization of text data.
• Feature Extraction: Techniques such as TF-IDF or Bag-of-Words.
• Model Training: Includes popular machine learning algorithms for classification.
• Evaluation: Metrics such as accuracy, precision, recall, and F1-score.

Installation

1. Clone this repository:

   git clone https://github.com/your-username/SPAM_SMS.git

2. Navigate to the project directory:

   cd SPAM_SMS

3. Install the required packages:

   pip install -r requirements.txt

Usage

Run the notebook:

jupyter notebook SPAM_SMS.ipynb

Dataset

Ensure that the dataset (e.g., spam_sms.csv) is placed in the same directory as the notebook. The dataset should have columns for the text messages and their respective labels.

Results

The notebook includes visualizations and performance metrics demonstrating the effectiveness of the trained model.

Contributions

Feel free to submit pull requests to enhance the project.