Spam-Ham Classification using Naive Bayes

This repository contains a Spam-Ham classification project using Naive Bayes classifiers (MultinomialNB and BernoulliNB) from scikit-learn.

Table of Contents

• Introduction
• Dataset
• Installation
• Usage
• Model
• Evaluation
• Contributing
• License

Introduction

Spam detection is a common NLP problem where emails, messages, or text data are classified as either spam or ham (not spam). This project implements a simple classifier using Naive Bayes algorithms.

Dataset

The dataset used for training and testing consists of labeled text messages indicating whether they are spam or ham. A common dataset used for this task is the SMS Spam Collection.

Installation

Clone the repository and install the dependencies:

git clone https://github.com/your-username/spam-ham-classifier.git
cd spam-ham-classifier
pip install -r requirements.txt

Usage

Run the classification script:

python spam_ham_classifier.py

Model

This project uses two types of Naive Bayes classifiers:

1. MultinomialNB - Best suited for text classification problems with term frequency features.
2. BernoulliNB - Works well with binary term occurrence features.

The text data is processed using the following steps:

• Tokenization
• Stopword removal
• TF-IDF vectorization

Evaluation

The model is evaluated using common metrics such as:

• Accuracy
• Precision
• Recall
• F1-score

Results are printed at the end of the script.

Contributing

Contributions are welcome! Feel free to fork the repository and submit pull requests.

License

This project is licensed under the MIT License.