Email Spam Detection

This project implements a Naive Bayes classifier for detecting spam emails. The model is trained using a dataset of emails, which are classified as either spam or ham (non-spam). The classifier uses the frequency of specific words in the emails to predict the likelihood of an email being spam.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Data
• Results
• License
• Contact

Introduction

Email spam detection is an important application of machine learning, helping to filter out unwanted emails and improve user experience. This project utilizes a Naive Bayes approach to classify emails based on the frequency of certain words.

Features

• Implements a Naive Bayes classifier for spam detection.
• Trains on a dataset of labeled emails.
• Calculates the probability of each word being in a spam or ham email.
• Classifies new emails based on the trained model.

Installation

To get started with this project, follow these steps:

1. Clone the repository:

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

2. Navigate to the project directory:

   cd email-spam-detection

3. Ensure you have a C++ compiler installed.

Usage

1. Compile source folder:

   g++ spamDetect.cpp Email.cpp -o spamDetect

2. Run the program:

   ./spamDetect

Data

The dataset consists of two files: training_set.data and test_set.data. Each file contains emails represented by the frequency of certain words and a label indicating whether the email is spam (0) or ham (1).

Result

The program outputs the following statistics after running the classification:

• Total number of emails in the training and test sets.
• Number of spam and ham emails in the training and test sets.
• Number of correct and incorrect classifications.
• Prediction accuracy.

License

This project is licensed under the MIT License.

Contact

• GitHub: amritBskt
• Email: your-amritbskt7@gmail.com