atreop/spam-email-detection

Spam-Email-Detection

Overview

This GitHub project aims to implement a spam email detection system using the Naive Bayes algorithm. The Naive Bayes classifier is a probabilistic machine learning model that is particularly well-suited for text classification tasks, making it a popular choice for spam detection.

Table of Contents

• Getting Started
• Prerequisites
• Installation
• Usage
• Dataset
• Algorithm
• Performance Evaluation

Getting Started

These instructions will help you get a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

Before running the project, make sure you have the following dependencies installed:

• Python (version 3.x recommended)
• Jupyter Notebook (optional, for exploring and visualizing data)

Installation

Clone the repository to your local machine:

git clone https://github.com/your-username/spam-email-detection.git

Change into the project directory:

cd spam-email-detection

Install the required Python packages:

pip install pandas
pip install sklearn

Usage

1. Open the Jupyter Notebook (spam_email_detection.ipynb) to see the step-by-step implementation of the Naive Bayes algorithm for spam email detection.

2. Run the cells in the notebook to train the model and evaluate its performance.

3. You can also use the trained model for predicting whether a new email is spam or not by following the provided examples.

Dataset

The project uses a labeled dataset for training and testing the Naive Bayes classifier. The dataset is available on Kaggle.

Algorithm

The Naive Bayes algorithm is implemented for spam email detection. The model assumes independence between features and calculates the probability of an email being spam based on the presence of certain words.

Performance Evaluation

The performance of the Naive Bayes classifier is evaluated using metrics such as accuracy, precision, recall, and F1 score. These metrics are displayed in the Jupyter Notebook for easy interpretation.